Artificial Intelligence in Floating Offshore Wind Turbines: A Critical Review of Applications in Design, Monitoring, Control, and Digital Twins

Abstract

Floating offshore wind turbines (FOWTs) face complex aero-hydro-servo-elastic interactions that challenge conventional modeling, monitoring, and control. This review critically examines how artificial intelligence (AI) is being applied across four domains—design and surrogate modeling, structural health monitoring, control and operations, and digital twins—with explicit attention to uncertainty and reliability. Using PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a Scopus search identified 412 records; after filtering for articles, conference papers, and open access, 115 studies were analyzed. We organize the literature into a taxonomy covering classical supervised learning, deep neural surrogates, physics-informed and hybrid models, reinforcement learning, digital twins with online learning, and uncertainty-aware approaches. Neural surrogates accelerate coupled simulations; probabilistic encoders improve structural health monitoring; model predictive control and trust-region reinforcement learning enhance adaptive control; and digital twins integrate reduced-order physics with data-driven calibration for lifecycle management. The corpus reveals progress but also recurring limitations: simulation-heavy validation, inconsistent metrics, and insufficient field-scale evidence. We conclude with a bias-aware synthesis and propose priorities for future work, including shared benchmarks, safe RL with stability guarantees, twin-in-the-loop testing, and uncertainty-to-decision standards that connect model outputs to certification and operational risk.

1. Introduction

Floating offshore wind turbines (FOWTs) have emerged as a pivotal technology to unlock wind resources in deep waters, where conventional fixed-bottom foundations are infeasible. Over the last decade, prototypes and pilot farms have demonstrated the feasibility of spar, semi-submersible, and tension-leg platforms, yet the engineering challenges remain formidable: coupled aero-hydro-servo-elastic interactions, non-stationary environmental loads, complex mooring and cable dynamics, and demanding operational and maintenance conditions [1]. These dynamics strain conventional modeling pipelines such as OpenFAST or high-fidelity CFD, which, while accurate, are computationally prohibitive for design optimization, uncertainty quantification, or controller-in-the-loop simulation.

Artificial intelligence (AI) has therefore been proposed as a complementary paradigm. Surrogate models based on neural networks or Gaussian processes can approximate hydrodynamic responses and fatigue life with orders-of-magnitude speed-up compared to CFD [2,3]. Structural health monitoring (SHM) increasingly leverages machine learning to detect damage from SCADA or sensor data, moving from deterministic classifiers to uncertainty-aware encoders [4]. In the control domain, reinforcement learning (RL) has been tested for pitch and torque regulation under stochastic seas, with conservative formulations such as trust-region methods improving stability [5]. Digital twin frameworks integrate these methods by coupling reduced-order physics with AI calibration layers, enabling real-time updating, remaining useful life (RUL) prediction, and even fleet-level coordination [6,7].

Despite this momentum, the field remains fragmented. Many studies rely exclusively on simulations, raising questions of external validity [1]. Others emphasize accuracy metrics without evaluating uncertainty calibration or operational impact, creating potential biases when models are deployed offshore. A critical, bias-aware review is therefore needed to synthesize the state of the art, identify robust contributions, and highlight gaps between simulation and practice.

The objectives of this review are threefold. First, to provide a systematic mapping of the literature following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, based on a Scopus search conducted on 3 October 2025, which yielded 115 open-access articles and conference papers after screening [1,2,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116]. Second, to propose a taxonomy of AI methods for FOWTs—supervised and classical machine learning, deep neural surrogates, physics-informed and hybrid models, reinforcement learning, digital twins, and uncertainty-aware approaches—and to analyze how these have been applied in design, monitoring, control, and lifecycle management. Third, to critically evaluate methodological and contextual biases using a Model–Outcome–Context (MOC) framework and to outline a research agenda that connects AI innovation with the certification and operational demands of offshore wind.

2. Background

Floating offshore wind turbines (FOWTs) extend the reach of offshore wind power into waters deeper than 60 m, where fixed-bottom foundations are no longer feasible. Three primary platform concepts dominate: spar-buoy, semi-submersible, and tension-leg platforms (TLPs), each with distinct trade-offs in stability, cost, and hydrodynamic behavior [83]. The dynamic environment of the open ocean exposes these structures to combined aerodynamic, hydrodynamic, and structural loads, resulting in a highly coupled aero-hydro-servo-elastic system. Conventional simulation frameworks such as OpenFAST or CFD can capture these dynamics with accuracy, but at prohibitive computational cost, especially when applied to fatigue analysis, uncertainty quantification, or controller-in-the-loop simulations [10].

This complexity is amplified by mooring systems and dynamic cables, which govern station-keeping and power transmission. Accurate modeling of mooring line fatigue and lazy-wave cable behavior is crucial to ensure reliability, yet traditional methods rely on large ensembles of time-domain simulations, which are computationally intensive [95]. The same challenge applies to predicting extreme loads and rare-event responses, where brute-force Monte Carlo approaches are infeasible.

In operations, the control problem is equally demanding. Platform motions interact with rotor aerodynamics, introducing instabilities such as negative damping in pitch motion. Classical linear controllers designed for fixed-bottom turbines often perform poorly when transferred to floating platforms [52]. To address this, more adaptive approaches—nonlinear control, robust sliding mode, and reinforcement learning—have been proposed [5]. These strategies must account for constraints such as actuator saturation, time delays, and the stochastic nature of environmental disturbances.

The monitoring and maintenance dimension is another challenge. Offshore assets face limited accessibility, high downtime costs, and safety risks during inspections. Structural health monitoring (SHM) aims to detect damage early using vibration, strain, or SCADA data. Traditional thresholding methods often fail under varying sea states, motivating the adoption of machine learning for anomaly detection and prognostics [64].

Digital twins are emerging as integrative frameworks that combine physics-based models with AI calibration, enabling real-time updates and predictive capabilities. Twins can support decision-making in design, operations, and remaining useful life (RUL) assessment [6]. However, they require efficient surrogate models, robust uncertainty quantification, and secure data pipelines—domains where AI methods are increasingly vital.

The FOWT sector is characterized by high model complexity, costly computations, and stringent reliability requirements. These factors create a fertile ground for AI methods, which promise efficiency and adaptability, but whose adoption must be carefully assessed against the demands of safety-critical offshore systems.

3. Methods

3.1. Literature Search and PRISMA Workflow

The review followed the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for systematic reviews. A comprehensive search was conducted in Scopus on 3 October 2025 using a query designed to capture the intersection of floating offshore wind turbines (FOWTs) and artificial intelligence (AI). Although PRISMA 2020 is traditionally used in meta-analyses, here it is integrated with quantitative bibliometric mapping and domain-specific taxonomy construction, enabling a transparent yet exploratory synthesis. This hybrid approach combines the reproducibility of systematic reviews with the interpretive depth of conceptual analysis.

The search string included combinations of terms related to floating wind platforms (“floating offshore wind”, “floating wind turbine*”, “FOWT”, “spar”, “semi-sub*”, “tension leg”, “barge”) and AI methods (“artificial intelligence”, “machine learning”, “deep learning”, “reinforcement learning”, “digital twin”, “surrogate model”, “physics-informed”, “Gaussian process”, “support vector”, “random forest”, “Bayesian optimization”). To maintain focus on wind energy, tidal and wave energy studies were explicitly excluded.

Final Scopus quera used:

• TITLE-ABS-KEY ( ( “floating offshore wind” OR “floating wind” OR “floating wind turbine*” OR FOWT OR ( ( spar OR “semi-sub*” OR semisub* OR “tension leg” OR TLP OR barge ) W/3 ( turbine OR platform OR floater ) ) ) AND ( “artificial intelligence” OR “machine learning” OR “deep learning” OR “neural network*” OR “reinforcement learning” OR “digital twin*” OR “surrogate model*” OR “reduced-order model*” OR “physics-informed” OR PINN* OR “Gaussian process*” OR kriging OR “support vector*” OR “random forest” OR “gradient boosting” OR “Bayesian optimization” ) AND ( design OR optimization OR “structural health” OR monitoring OR SHM OR diagnostics OR control OR controller OR “model predictive control” OR MPC OR “fault detection” OR “condition monitoring” OR “O&M” OR operations OR maintenance OR “state estimation” ) ) AND NOT TITLE-ABS-KEY ( “tidal” OR “wave energy” OR “point absorber” ) AND ( LIMIT-TO ( DOCTYPE , “ar” ) OR LIMIT-TO ( DOCTYPE , “cp” ) ) AND ( LIMIT-TO ( OA , “all” ) ).

Full path to Scopus search—accesed on 3 October 2025 [117].

The initial search returned 412 documents. After filtering for articles and conference papers, the number was reduced to 359. Restricting further to open-access publications yielded 115 studies, which formed the final dataset for full-text analysis. These 115 documents constitute the evidence base of this review. The PRISMA flow diagram is shown in Figure 1, illustrating the screening stages and inclusion/exclusion decisions.

To ensure replicability and reusability, only open-access (OA) studies with accessible full texts were included. This design choice enabled transparent data extraction and validation but may have introduced publication bias, excluding high-impact non-OA works. We therefore explicitly report the OA attrition step in Figure 1 and treat our synthesis as conservative relative to the broader literature that includes restricted-access publications.

3.2. Inclusion and Exclusion Criteria

Studies were included if they fulfilled the following criteria:

Applied AI, ML, or data-driven techniques directly to problems in floating offshore wind turbine design, monitoring, control, or lifecycle management;

Reported sufficient methodological detail to enable assessment of model design and outcomes;

They were published as peer-reviewed articles or conference papers in English.

Studies were excluded if they fulfilled the following criteria:

-

Focused exclusively on fixed-bottom turbines or other marine renewables (e.g., tidal, wave energy, point absorbers);

-

Described general AI frameworks without direct application to FOWTs;

-

Lacked an accessible full text.

The exclusion process (Figure 1) was implemented through a two-stage workflow. Stage A—Automated filtering: duplicates were removed by DOI/title; language restricted to English; document types limited to articles and conference papers; off-topic items (e.g., fixed-bottom turbines or non-AI methods) excluded using regular-expression title filters.

Stage B—Manual relevance screening: Two independent reviewers examined full texts for explicit integration of AI/ML with floating offshore wind turbines. Borderline cases were resolved by consensus with the senior author. All numbers shown in Figure 1 correspond to the sequential counts at each exclusion step.

3.3. Data Extraction and Bibliometric Analysis

For each study, bibliographic data (title, authors, year, affiliation, funding, source, DOI) and methodological details (AI technique, application domain, validation approach, reported metrics) were extracted. Bibliometric indicators such as annual publication trends, citation counts, country and institutional distribution, and funding sponsors were compiled to characterize the research landscape.

3.4. Bias Evaluation: The MOC Framework

In addition to standard bibliometric analysis, we evaluated methodological quality and potential bias using a Model–Outcome–Context (MOC) framework. This three-axis evaluation was adapted from bias assessment approaches in applied AI reviews:

Model bias: limitations arising from the choice of algorithm, training data volume, and feature representation (e.g., black-box deep models with no interpretability mechanisms).

Outcome bias: issues related to the selection and reporting of performance metrics (e.g., reliance on RMSE without uncertainty calibration, inconsistent validation splits).

Context bias: the gap between experimental validation and real-world deployment (e.g., simulation-only studies, lack of field data, absence of reliability assessment).

Each included study was qualitatively assessed against these dimensions to inform the critical synthesis presented in Section 6, Section 7 and Section 8.

4. Bibliometric Landscape

4.1. Temporal Trends

The application of AI to floating offshore wind turbines is a relatively young research field, with the earliest open-access studies appearing in 2014. Activity remained minimal until 2018, when the first wave of conference papers began exploring surrogate modeling and control schemes. A modest increase followed in 2019–2020 (3–5 documents annually), coinciding with rising attention to floating demonstrator projects in Europe and Asia. From 2021 onward, publication counts grew sharply: 12 documents in 2021, 21 in 2022, and 29 in 2024, with a further acceleration in 2025 (17 studies by October). A single study is already indexed for 2026, suggesting continued momentum. This trajectory indicates that AI for FOWTs is transitioning from exploratory applications to a mature research domain with a critical mass of contributions (Figure 2).

Citation patterns reflect the same acceleration. Early work before 2018 had negligible impact, but recent papers have received rapid uptake. Total citations across the 115 open-access studies exceed 2100, with over 550 citations added in 2023 alone. Highly cited contributions are concentrated in design surrogates and control methods, reflecting the community’s urgent need for efficient models and robust controllers (Figure 3).

4.2. Geographical Distribution

The research effort is globally distributed but clustered in a few leading regions. China (17 documents), the United Kingdom (17), Norway (16), and the United States (15) account for more than half of all publications. Other strong contributors include Spain (14), Italy (10), France (9), the Netherlands (9), Germany (8), and South Korea (8). Northern Europe is particularly active due to offshore wind deployment projects in the North Sea and North Atlantic. Emerging contributions from Portugal, Denmark, and Australia signal broadening interest, while single contributions from Nigeria, Greece, and Hong Kong highlight the global reach of the topic (Figure 4).

The dominance of China, Norway, the UK, and the USA can be linked to several structural factors. First, these nations have implemented large-scale public funding programs and dedicated demonstration sites for floating offshore wind technology. Second, Norway and the UK host the most active full-scale testbeds (e.g., Hywind Scotland, Hywind Tampen), while the USA and China maintain broad industrial supply chains and university–industry partnerships. Third, national strategic roadmaps and net-zero policies explicitly target offshore wind expansion, encouraging cross-disciplinary AI research. The dispersion of Chinese institutions visible in Figure 5 suggests a distributed ecosystem of many contributors, whereas the Norwegian and British outputs originate from a few well-consolidated research hubs.

4.3. Institutional Landscape

At the institutional level, research is concentrated in universities and specialized centers with strong offshore engineering programs. NTNU (Norway, 12 documents) leads, reflecting Norway’s strategic investments in floating offshore wind. Delft University of Technology (8), University of Stuttgart (7), and the University of Strathclyde (6) follow, all of which are major players in offshore engineering and control. Notable contributions also come from the Basque Center for Applied Mathematics (6) and industrial–academic partnerships such as Tecnalia (5) and the Basque Research and Technology Alliance (5). Industrial actors, including DNV, Siemens Gamesa Renewable Energy, Stiesdal Offshore Technologies, and Goldwind, appear in single but strategically important publications, demonstrating growing engagement of the private sector. Although China leads in total publication count, its contributions are distributed across numerous institutions, leading to lower individual institutional visibility. This fragmentation contrasts with concentrated research networks in Norway and the UK, where fewer institutions account for most publications.

4.4. Funding Sponsors

Funding analysis reveals the strong influence of public research programs. The EU Horizon 2020 Framework Programme supported at least 10 studies, complemented by Marie Skłodowska-Curie Actions (5) and the European Regional Development Fund (5). National agencies are equally prominent: the National Natural Science Foundation of China (8), the Spanish Agencia Estatal de Investigación (7), the U.S. Department of Energy (6), and the Norwegian Research Council (3). Sector-specific sponsors include Equinor, DNV, and the UK SuperGen Marine Energy Research Centre, which appear in targeted collaborations. This funding pattern shows that AI in floating wind is not a fringe activity but embedded within major strategic energy programs (Figure 6).

4.5. Bibliometric Synthesis

Overall, the bibliometric landscape underscores that AI for FOWTs is a fast-emerging interdisciplinary domain. It is led by a small group of European and Asian institutions, supported by international funding, and marked by rapid acceleration since 2020. The field remains concentrated in design surrogates, monitoring, and control, with digital twins gaining traction since 2023. Industrial participation is growing, but the majority of contributions remain academic, raising questions about technology transfer and deployment readiness.

5. Taxonomy of AI Methods for FOWTs

The adoption of artificial intelligence in floating offshore wind research has not been monolithic. Rather, it has evolved through distinct methodological families, each bringing different strengths and vulnerabilities. In this section, we trace the trajectory of these methods—beginning with classical supervised learning, moving through deep surrogates, hybrid and physics-informed approaches, adaptive reinforcement learning controllers, digital twins, and finally uncertainty-aware frameworks. Together, they represent not only technical diversity but also the gradual maturation of AI from a supportive tool to an integral component of design and operations.

5.1. Supervised and Classical Machine Learning

The early use of artificial intelligence in floating offshore wind turbines (FOWTs) drew heavily on supervised learning and classical regression models, which offered interpretable, data-efficient tools for understanding hydrodynamic and structural behavior. These methods provided simplified yet physically grounded estimators when full numerical simulations were either computationally prohibitive or lacked sufficient calibration data.

Zhao et al. (2025) [95] optimized lazy-wave dynamic cable systems under coupled hydrodynamic and aerodynamic loads using a regression-assisted design framework, improving reliability while reducing simulation costs.

At the mooring system level, Coraddu et al. (2024) [60] employed regression and classification techniques to segment and predict mooring line behavior, combining physical modeling with supervised learning for condition-aware design.

In the structural domain, Feng et al. (2024) [41] applied reduced-order surrogate modeling for multi-parameter design optimization, demonstrating that response-surface approximations can reproduce dynamic responses of floating turbines with high accuracy and minimal data.

Earlier, Mao et al. (2022) [80] had shown how classical response surface methodology, combined with genetic algorithms, enables robust multi-objective optimization of semi-submersible platforms, balancing performance and cost in conceptual design.

Together, these studies underscore the foundational role of classical machine learning in the evolution of AI for FOWTs. They demonstrate how interpretable regression and surrogate-based models laid the groundwork for later deep and hybrid approaches, allowing engineers to explore design spaces efficiently while maintaining physical transparency and numerical stability.

5.2. Deep Learning and Neural Surrogates

The shift toward deep learning expanded the modeling frontier for FOWTs, allowing complex dynamic interactions between aerodynamic, hydrodynamic, and structural subsystems to be learned directly from data. Neural surrogates, recurrent architectures, and hybrid networks began to replace low-capacity predictors and rule-based control loops.

Kang, Park, and Kwon (2025) [66] introduced an attention-based deep learning model to predict the dynamic responses of floating platforms, achieving solver-level accuracy while retaining interpretability through attention weights.

Didier et al. (2025) [84] developed a neural network-based super-twisting controller that combines deep representations with nonlinear control theory, enhancing robustness against environmental disturbances. In reliability and fatigue assessment, Ren and Xing (2025) [1] proposed an active-learning framework that integrates deep regression networks with uncertainty-guided sampling to predict fatigue damage with limited simulation data.

Beyond control and prediction, Zhao et al. (2025) [95] introduced a Gaussian mixture autoencoder for structural health monitoring, explicitly quantifying uncertainty in anomaly detection. Barooni et al. (2025) [88] presented a hybrid deep learning model combining convolutional and recurrent neural networks to forecast FOWT power output under varying metocean conditions.

These advances underscore how deep learning enables FOWTs to capture dynamic dependencies and long-term temporal patterns beyond the reach of classical ML approaches, though often at the cost of explainability and generalization across platforms.

5.3. Physics-Informed and Hybrid Models

As deep learning matured, the need for physically grounded modeling became increasingly evident. Concerns over the opacity of black-box networks encouraged the development of physics-informed and hybrid architectures, in which machine learning complements rather than replaces established numerical models. These frameworks embed physical principles into data-driven learning to improve interpretability, reduce data dependency, and preserve consistency with governing dynamics.

Sun et al. (2025) [67] proposed a hybrid inversion framework for identifying the constitutive relations of wind turbine blade spars with wrinkle defects.

Their approach coupled finite element analysis with a Bayesian-regularized neural network optimized via particle swarm optimization (PSO) to retrieve true stress–strain parameters from experimental tests.

The method achieved parameter errors below 5% and accurately reconstructed mechanical responses, demonstrating the potential of hybrid neural–physics inversion for structural reliability assessment.

Ilardi et al. (2024) [31] introduced computationally aware surrogate models that integrate reduced-order solvers with machine learning correction layers to accelerate coupled aero-hydrodynamic simulations while maintaining numerical fidelity.

This hybridization bridges the speed of data-driven surrogates with the trustworthiness of physics-based computation.

Similarly, Baudino Bessone et al. (2024) [112] applied surrogate-assisted optimization to floating turbine design, embedding analytical hydrodynamic constraints within regression-based surrogates to ensure physically plausible extrapolations.

Their framework demonstrated how design efficiency and interpretability can coexist in multi-objective optimization. At the component scale, Zhao et al. (2025) [95] embedded physics-informed reliability constraints into the optimization of lazy-wave dynamic cables, coupling surrogate models with probabilistic formulations to ensure structural safety during dynamic operations.

These studies mark a decisive turn toward hybrid intelligence—approaches that blend physical knowledge and data-driven inference to achieve models that are not only accurate but also credible, explainable, and certifiable for engineering practice.

5.4. Reinforcement Learning and Adaptive Control

Control has long been central to floating offshore wind turbine (FOWT) operation, where the interplay between aerodynamic, hydrodynamic, and structural dynamics challenges the direct transfer of fixed-bottom control strategies. Traditional PID or model-predictive controllers often lack robustness to nonlinear platform motion and stochastic disturbances. Consequently, reinforcement learning (RL) and adaptive control have become prominent solutions for achieving self-tuning, resilient regulation under complex sea-state variability.

Didier et al. (2026) [5] introduced a Trust Region Policy Optimization (TRPO)-based pitch controller that constrains policy updates to maintain dynamic stability during online learning. The controller adapts to turbulence and coupled wind–wave excitation while preserving safety guarantees through bounded gradient steps. The study demonstrated substantial reductions in platform pitch excursions and fatigue loads compared with conventional controllers.

Earlier, Didier et al. (2023) [39] validated a deep reinforcement-learning (DRL) framework that couples actor–critic networks with a high-fidelity digital twin of the turbine. The algorithm jointly optimizes pitch and torque control, converging faster and showing greater robustness than gain-scheduled or linear baselines.

Complementing these advances, Yoon et al. (2025) [54] applied model predictive control (MPC) enhanced by directional wave-spectra estimation, embedding environmental forecasts directly into the control horizon. The hybrid, data-assisted MPC reduced tower-top displacement and blade-root bending moments under irregular sea conditions.

At the observer level, Gräfe, Pettas, Dimitrov, and Cheng (2024) [74] developed machine learning-based virtual load sensors for mooring lines, combining motion simulations from OpenFAST and synthetic LIDAR data from ViConDAR. Using long short-term memory (LSTM) and convolutional neural network (CNN) architectures, they accurately reconstructed fairlead tension time series and damage-equivalent loads (DELs). The study found that while motion measurements alone provide the best accuracy, LIDAR data can substitute when physical sensors are unavailable.

Expanding the notion of safe exploration, Chen et al. (2021) [109] introduced a software-in-the-loop combined reinforcement learning and model-based control architecture for wind-energy applications. Their approach used constrained policy optimization together with physical models to enforce safety boundaries during training, illustrating how “safe RL” concepts can bridge the gap between simulation and field deployment.

These contributions position reinforcement learning as a transformative paradigm for FOWT control—capable of learning directly from dynamic interactions rather than relying solely on predefined gain schedules. Future progress will likely depend on hybrid RL architectures that blend the adaptability of deep learning with physics-based constraints, ensuring stability, transparency, and certifiability for real-world offshore operations.

5.5. Digital Twins and Online Learning

Digital twins represent the most integrative frontier of artificial intelligence in floating offshore wind systems. They combine physics-based simulation, sensor data assimilation, and machine learning calibration to create continuously evolving virtual counterparts of turbines and farms. These twins enable the seamless coupling of design, operation, and maintenance through online synchronization with real-world data, providing an adaptive link between digital modeling and physical performance.

Pacheco-Blázquez et al. (2024) [6] developed an open-source digital twin for real-time structural-state control and remaining useful life (RUL) forecasting in composite offshore turbine structures. Their framework integrates fatigue-state tracking and data-driven monitoring using sensor inputs and lifecycle models. The system effectively predicts degradation patterns and supports condition-based maintenance while reducing unnecessary inspections and operational downtime. The study demonstrated the feasibility of scalable digital twin software for offshore assets, highlighting its role in Industry 4.0–driven operational sustainability.

At the component level, Fernández-Navamuel et al. (2025) [64] applied a Gaussian mixture autoencoder (GMAE) to the structural response of a floating offshore wind turbine for uncertainty-aware damage identification. Although not a full digital twin, this architecture serves as a twin-analog by learning latent fault signatures directly from vibration data, while quantifying prediction uncertainty. Such unsupervised systems bridge the gap between condition monitoring and digital twin technology by providing adaptive, self-calibrating anomaly detection under varying metocean loads.

Scaling up from single-turbine applications, Kandemir, Liu, and Hasan (2023) [7] presented a digital twin–driven dynamic repositioning framework for multi-floater wind farms. The model integrates metocean forecasts, hydrodynamic simulations, and control optimization to continuously adjust the positioning of floating platforms. This real-time feedback allows the wind farm to maximize energy yield while mitigating wake interference and extreme-load events, providing a path toward predictive and adaptive farm-level operations [7].

Recent research trends extend these efforts toward online learning and adaptive synchronization. Modern twins increasingly embed incremental learning, Bayesian updating, or transfer learning techniques to maintain calibration without full retraining, accommodating operational drift caused by component wear, seasonal changes, or evolving control policies. Such self-updating capabilities mark the transition from static “digital shadows” to autonomous digital twins, which can actively forecast, self-correct, and guide decision-making across the turbine’s entire lifecycle.

These studies demonstrate that digital twins in floating wind are no longer confined to visualization or simulation—they are evolving into intelligent, self-learning agents that fuse physical models with adaptive AI. Their success, however, depends on the rigorous integration of physics-informed constraints and verified data flows, ensuring that predictive power does not come at the expense of interpretability, traceability, or certification. The most promising direction for the coming decade lies in hybrid digital twin ecosystems, where deep learning augments—but never replaces—the established foundations of computational fluid dynamics, structural mechanics, and control theory.

5.6. Uncertainty and Reliability-Oriented AI

As AI applications in floating offshore wind mature, a defining frontier lies in the explicit integration of uncertainty quantification (UQ) and reliability analysis. Moving beyond deterministic surrogates, recent studies focus on how AI systems can capture epistemic and aleatory uncertainty, enabling risk-informed design and maintenance of floating wind systems.

Cousin et al. (2024) [94] proposed an optimal design of experiments framework to efficiently estimate the failure probability of offshore structures. Their approach leverages adaptive sampling guided by probabilistic models to reduce the computational cost of reliability assessment while maintaining accuracy. This work bridges the gap between statistical learning and traditional structural safety engineering, establishing data-driven pathways to quantify uncertainty in failure analysis.

Complementing this, Rowell and McMillan (2024) [26] revisited systems safety principles in offshore wind design, emphasizing how machine learning can support probabilistic reliability evaluation. Their study integrates AI-based diagnostics within established reliability frameworks, demonstrating that predictive models can augment—but not replace—formal safety assurance methods.

From a modeling standpoint, Palma et al. (2025) [23] introduced robust AI models for system identification and load forecasting in marine structures. Their method applies ensemble and regularization techniques to ensure numerical stability under noisy data conditions. These robustness-oriented designs address the critical issue of variance amplification and are foundational to trustworthy AI deployment in offshore applications.

Focusing on the origin of modeling uncertainty, Yang et al. (2025) [68] systematically investigated uncertainty localization methods for numerical simulations of offshore structures. They analyzed how parameter sensitivity, numerical discretization, and stochastic boundary inputs contribute to prediction variance. The study offers practical insight into where machine learning models should prioritize adaptive calibration and error control.

Alkarem and Huguenard (2024) [91] demonstrated that digital twins incorporating uncertainty quantification can unify physical modeling and AI calibration. Their predictive twin architecture dynamically updates probability distributions of model parameters, enabling real-time confidence assessment for decision-making under uncertainty.

These contributions outline a coherent paradigm for reliability-oriented AI: algorithms that not only predict but also express confidence, sensitivity, and robustness. Integrating UQ into digital-twin ecosystems and AI controllers ensures that predictions align with the probabilistic thinking essential to offshore reliability engineering. The field’s next challenge lies in calibration verification—ensuring that uncertainty estimates are statistically valid and actionable across design, monitoring, and operational phases.

5.7. Synthesis

The progression of artificial intelligence in floating offshore wind research mirrors the evolution of trust and capability within engineering modeling itself. Classical machine learning provided the first structured entry point—offering interpretability, robustness, and computational accessibility when physical models were incomplete. The subsequent rise in deep neural surrogates extended the predictive horizon, enabling high-fidelity emulation of complex aero-hydrodynamic interactions. Yet, their opacity prompted a return to interpretability through physics-informed and hybrid frameworks, in which learning augments, rather than replaces, first-principles formulations. Reinforcement learning introduced adaptability, allowing controllers to learn optimal behavior directly from system feedback under stochastic sea states. Digital twins then unified these developments into continuously updated virtual counterparts of turbines and farms, transforming AI from an analytical tool into an operational companion. Finally, the explicit inclusion of uncertainty and reliability frameworks reconnected data-driven inference with risk-aware engineering, a prerequisite for real-world deployment and certification.

Taken together, these methodological families no longer compete but increasingly coexist. The trajectory of the field points toward integrated, hybrid intelligence—ecosystems in which physics, data, and learning are interwoven across the turbine lifecycle. The future of AI in floating wind will depend less on algorithmic novelty than on architectural credibility: the ability to deliver transparent, verifiable, and trustworthy systems that can inform design, monitoring, and control without compromising safety or interpretability.

6. Applications of AI in Floating Offshore Wind Turbines

Artificial intelligence has found application across the full lifecycle of floating offshore wind turbines, from conceptual design through operations and maintenance. The 115 studies reviewed reveal four dominant application clusters: design and optimization, structural health monitoring, control and operations, and digital twins. Each reflects not only technical opportunity but also specific industrial challenges.

6.1. AI in Design and Surrogate-Based Optimization

Design remains one of the most mature areas of AI application in floating wind. Surrogate models and optimization frameworks are used to accelerate design iterations that would otherwise depend on computationally expensive coupled aero-hydro-servo simulations. Bayat, Lee, and Allison (2025) [83] show that nested control co-design can jointly optimize structural and controller parameters for a spar-type FOWT, reducing platform motions and loads while preserving energy capture. At the hydrodynamics level, Wei et al. (2024) [49] develop a surrogate for fluid load prediction on a moored spar in uniform current, enabling fast dynamic analyses that are consistent with high-fidelity solutions yet orders of magnitude cheaper to evaluate. Focusing on extremes relevant to design, Wang et al. (2023) [27] use artificial neural networks to predict extreme responses of FOWTs under operating conditions, providing rapid estimators that support ultimate-limit-state checks during early-stage design. Complementarily, Okpokparoro and Sriramula (2023) [105] perform reliability analysis of dynamic cables under realistic environmental loads, clarifying how statistical variability and loading uncertainty should inform surrogate-based design margins.

AI accelerates exploration of large design spaces and enables rapid what-if studies; however, credibility still hinges on validation against experimental or field data and on explicit uncertainty treatment when surrogates inform certification-relevant decisions.

6.2. Structural Health Monitoring and Diagnostics

Structural health monitoring (SHM) in floating offshore wind turbines (FOWTs) hinges on detecting early damage under harsh, nonstationary marine conditions. Recent AI work targets robust feature learning from vibration, motion, and SCADA streams, and it increasingly couples diagnostics with explicit uncertainty information for risk-informed maintenance.

Sharma and Nava (2024) [32] developed a mooring-system condition-monitoring pipeline in which a neural-network framework is coupled with autoregressive (AR) coefficients. The AR layer captures temporal dynamics, while the network maps features to integrity indicators, enabling data-efficient detection of mooring degradations. For turbine- and farm-level controls, Fernández-Navamuel, Peña-Sanchez, and Nava (2024) [56] presented a supervised deep-learning methodology for fault detection and identification (FDI) across floating wind farms, improving classification of control-system faults under realistic sea-state variability. At the component level, Wang, Wen, and Wu (2024) [57] proposed fault detection and isolation for pitch systems that combine a Kalman filter with a multi-attention 1D-CNN, yielding accurate isolation of actuator/sensor faults while filtering environmental noise.

Pushing SHM toward risk-aware decisions, Fernández-Navamuel et al. (2025) [64] introduced a Gaussian mixture autoencoder (GMAE) for uncertainty-aware damage identification on a floating turbine. The model learns latent fault signatures and outputs class-conditional uncertainty, a key feature for maintenance prioritization.

Fathnejat and Nava (2025) [35] addressed the scarcity of labeled failure data with data augmentation for damaged scenarios in FOWTs, using hierarchical variational approximation to synthesize credible damaged samples and improve diagnostic generalization.

For instance, several studies, e.g., [78,85,97] demonstrated that reinforcement learning can outperform classical PID controllers in dynamic gust conditions by adapting to nonlinear aerodynamic feedback. Similarly, hybrid model-predictive control with surrogate-assisted optimization, e.g., [102,109], significantly reduced load oscillations, illustrating the tangible benefits of AI in real-time operational control.

SHM for floating wind is moving from deterministic classifiers to probabilistic, noise-robust pipelines that (i) encode temporal dynamics, (ii) deliver fault isolation rather than mere detection, and (iii) attach uncertainty measures to support risk-based maintenance—yet broad industrial uptake will still depend on shared, field-scale datasets.

6.3. Control and Operational Strategies

Control of floating offshore wind turbines (FOWTs) presents distinctive challenges, as the platform’s six-degree-of-freedom motion induces strong coupling between aerodynamic and hydrodynamic dynamics. Traditional fixed-bottom controllers—typically based on proportional–integral–derivative (PID) or linear model-predictive control—often fail to ensure stability under such nonlinear and stochastic conditions. Consequently, recent research has increasingly turned toward artificial-intelligence-based controllers capable of learning and adapting to environmental variability.

Didier, Laghrouche, and Dépernet (2026) [5] introduced a Trust Region Policy Optimization (TRPO)-based pitch controller for above-rated wind conditions, marking one of the first reinforcement-learning approaches explicitly applied to floating systems. Their framework constrained policy updates to preserve stability and safety during online learning, while achieving substantial reductions in fatigue loads and platform pitch motion.

Hybrid control strategies have also gained momentum as a compromise between interpretability and adaptability. Muñoz-Palomeque, Sierra-García, and Santos Peñas (2025) [52] developed a hybrid control scheme combining neural networks with classical controllers to optimize turbine performance under fluctuating wind regimes. The neural component adapted in real time to compensate for nonlinear aerodynamic effects, while the conventional controller maintained deterministic stability bounds.

In parallel, control co-design—the joint optimization of turbine and controller parameters—has emerged as a holistic approach to system-level performance. Bayat et al. (2025) [83] presented a nested control co-design of a spar-type FOWT that simultaneously tuned structural and control parameters to minimize platform motion and improve energy capture. This integration demonstrated that controller design cannot be decoupled from hydrodynamic and structural configuration.

Control enhancement is not limited to pitch and torque regulation. Yoon, Park, and Kim (2025) [54] proposed a complex-valued neural network framework to estimate directional wave spectra directly from platform motion data. This information was subsequently used to refine control inputs in real time, linking environmental perception to operational decision-making.

From a diagnostic standpoint, intelligent controllers are increasingly intertwined with condition monitoring. Korolis et al. (2025) [72] combined machine learning–based vibration diagnostics with control adaptation, using laboratory-scale FOWT experiments to show how learned vibration signatures can inform actuator behavior and mitigate emerging instabilities.

Collectively, these studies illustrate the convergence of control theory and artificial intelligence in FOWTs. Reinforcement learning introduces autonomy and adaptability, hybrid control merges data-driven correction with deterministic safety, and co-design frameworks integrate mechanical and control optimization within a unified loop. Future development will likely center on hybrid, physics-informed controllers that preserve certifiable safety while exploiting AI’s ability to navigate uncertainty and dynamic coupling in the floating environment.

6.4. Digital Twins for Lifecycle Management

The digital twin (DT) paradigm has evolved from an operational support tool into a central framework for lifecycle management of floating offshore wind systems. Recent studies emphasize that the value of digital twins lies in their ability to fuse heterogeneous data—structural, environmental, and operational—into continuously updated computational replicas that mirror asset behavior under real conditions. By enabling the seamless flow of information from design to decommissioning, DTs allow engineers to monitor degradation, predict failures, and plan maintenance within probabilistic risk bounds rather than through fixed inspection schedules.

In a 2025 study, Festa, Charles, and Masciola [10] developed a flexible neural-network-based surrogate model that forms the core of a lifecycle-aware digital twin. Their approach couples high-fidelity hydrodynamic solvers with adaptive machine learning layers to update structural response predictions in near real time. This integration allows the twin to adapt to gradual changes in sea-state conditions and operational loads, creating a living representation of the turbine’s structural health over time.

A complementary direction was proposed by Ren and Xing [1], who designed an active-learning framework to improve fatigue life estimation under uncertain loading conditions. Although their model was primarily intended for structural reliability assessment, its iterative enrichment and multi-point sampling mechanisms exemplify the self-learning capability essential for digital twins managing long-term degradation. The ability to refine predictions through data-driven feedback loops ensures that maintenance interventions are guided by statistically grounded confidence levels rather than deterministic thresholds.

From a systems perspective, Kang, Park, and Kwon [66] propose interpretable AI methods for predicting floating platform responses under coupled environmental loads. Their interpretable neural surrogate forms the analytical core of a decision-support twin that maintains transparency in data–model interactions, a prerequisite for certifiable lifecycle management in offshore engineering. By exposing sensitivity pathways between input parameters and predicted motions, this study demonstrates how explainable AI can make digital twins auditable and regulator-ready.

The transition toward integrated lifecycle management also requires embedding control and degradation modeling into unified frameworks. Didier, Laghrouche, and Dépernet [5] introduced a reinforcement-learning-based pitch controller that continuously updates its control policy based on operational feedback. When coupled with a digital twin, such adaptive control schemes can ensure that operational strategies evolve alongside structural conditions, closing the loop between monitoring and actuation. This self-updating synergy between control intelligence and digital replicas points toward the next generation of autonomous lifecycle management.

Equally significant is the shift toward hybrid intelligence, where physics-based simulation and AI coevolve within the same twin architecture. Didier, Basbas, and Lari [84] proposed a neural-network-based super-twisting controller integrated with nonlinear dynamic models of FOWTs. Although designed for control robustness, their architecture exemplifies how model-based digital twins can couple dynamic response prediction with control adaptation, enabling continuous assessment of both performance and reliability.

These developments signify a conceptual and technological maturation of digital twins—from static repositories of simulation data to dynamic, self-learning entities governing the full lifecycle of offshore wind assets. The emerging generation of digital twins unites interpretability, adaptivity, and uncertainty awareness, allowing maintenance to evolve from reactive to predictive and finally prescriptive. As regulatory frameworks begin to demand transparent, data-driven asset certification, lifecycle digital twins will not only support engineering efficiency but also define the digital infrastructure of sustainable offshore energy systems.

To contextualize the reviewed AI applications,

Table 1. Critical challenges and representative AI approaches.

Domain	Critical Point	Typical Limitation	Implication
Design/Surrogate modeling	Nonlinear coupled load prediction	Limited extrapolation ability outside training domain	Requires physics-informed constraints and uncertainty quantification
Structural-health monitoring	Sparse labels, sensor drift	Overfitting to noise	Semi-supervised/drift-aware ML models
Control and operations	Stability under extreme sea states	Safety constraint violation	Safe RL and robust MPC integration
Digital-twin lifecycle	Data–model mismatch	Calibration and identifiability issues	Hybrid physics + data twins with online recalibration

summarizes the major engineering and operational challenges where AI plays an enabling or complementary role.

7. Bias and Methodological Limitations

No systematic review is immune to bias, and the corpus of 115 open access studies on AI in floating offshore wind is no exception. Using a methodological quality framework, several recurring limitations become apparent.

The first source of bias lies in data provenance. A large fraction of the studies are validated exclusively on simulated data generated from numerical solvers such as FAST, OpenFAST, or in-house hydrodynamic codes. While simulation campaigns provide valuable reproducibility, they risk producing models that perform well under synthetic conditions but fail to generalize to real metocean environments. This simulation bias is visible across design optimization studies (e.g., [10,11,12,13,14,15,16]), reinforcement learning controllers [5]. The absence of large-scale offshore datasets amplifies this limitation.

A second bias emerges from geographical and institutional concentration. More than half of all publications originate from a small cluster of countries—China, the United Kingdom, Norway, and the United States—and leading institutions such as NTNU, TU Delft, and Stuttgart University. This concentration risks narrowing methodological diversity, as many approaches are developed under similar funding priorities and experimental infrastructures. Regions with significant floating wind potential, such as Africa and South America, remain almost absent from the corpus, skewing the knowledge base toward Northern European and East Asian contexts.

Third, there is evidence of publication and citation bias. Highly cited papers tend to focus on surrogate design and control, while structural health monitoring and uncertainty quantification receive fewer citations despite their industrial importance. This imbalance may reflect the preference of journals and conferences for performance-driven metrics rather than reliability-oriented research, inadvertently shaping the field’s trajectory.

Methodologically, a recurring limitation is inconsistent evaluation metrics. Some studies benchmark accuracy in terms of root mean square error, others report normalized mean absolute error, while control studies prioritize load reduction or power gain. Rarely are uncertainty intervals or calibration metrics reported. The lack of standardized benchmarks prevents rigorous cross-comparison, making it difficult to identify genuinely superior approaches.

Finally, the corpus suffers from limited industry involvement and field validation. While industrial partners such as Siemens Gamesa, Equinor, and DNV appear sporadically, the majority of contributions are academic. Consequently, few methods have been tested on operational turbines, and almost none have been deployed at a commercial scale. This academic bias leads to a gap between published potential and practical adoption.

7.1. Synthesis of Bias Evaluation

Taken together, these biases highlight the fragility of current evidence. AI methods for floating offshore wind are promising but still heavily dependent on simulation, geographically concentrated, and fragmented in evaluation practice. The field would benefit from international benchmark datasets, transparent reporting of uncertainty, and stronger collaboration between academia and industry. Addressing these limitations will be essential for AI to progress from proof-of-concept studies to reliable tools for certification and operational decision-making.

7.2. Analytical Synthesis and Practical Implications

Beyond identifying bias, the reviewed evidence was re-evaluated using a Model–Outcome–Context (MOC) framework that links AI model families to their targeted outcomes and validation environments.

The analysis also revisits several unresolved debates identified in the literature—notably the trade-off between explainability and performance, the generalization of data-driven models under extreme sea states, and the reproducibility of hybrid AI–physics pipelines.

The synthesis reveals clear maturity gradients: surrogate modeling and control have the highest publication density but remain simulation-bound; structural health monitoring shows fewer studies yet stronger experimental validation; and digital-twin initiatives are growing but remain pilot-scale.

This mapping exposes a critical credibility bottleneck—most studies optimize numerical metrics (e.g., RMSE, R²) without connecting them to engineering quantities such as load reduction or fatigue-life extension.

A shift toward decision-oriented evaluation is needed, where AI performance is expressed in engineering or certification-relevant terms.

Such framing allows results to inform operational and safety decisions rather than isolated model comparisons.

This perspective aligns with emerging consensus in the offshore AI community that model credibility and explainability must precede large-scale automation.

Figure 7 summarizes the maturity and validation level across the four main domains—design, SHM, control, and digital twins—showing that while surrogate and control studies dominate numerically, their validation remains limited to simulations. Conversely, SHM exhibits higher realism but a smaller sample size. This imbalance underlines the need for benchmark datasets and shared validation protocols.

In this sense, the MOC synthesis not only organizes the field but also clarifies its conceptual tensions—between accuracy and interpretability, autonomy and safety, simulation and certification.

As shown in Figure 7, 76% of design and surrogate studies and 65% of control-related papers rely exclusively on simulation data, illustrating the dominance of numerically constrained experimentation. In contrast, about one-third of SHM studies and 15% of digital-twin research already include field-scale validation, suggesting higher technological maturity. The resulting maturity gap reveals that publication volume is not directly correlated with real-world readiness. In particular, control and surrogate models are prolific but remain detached from physical testing and certification procedures, whereas SHM demonstrates slower but more evidence-based progress. These contrasts emphasize the need for harmonized validation standards and cross-domain benchmark datasets to accelerate trustworthy AI deployment in floating offshore wind systems.

The observed methodological diversity across AI applications reflects domain-specific priorities: surrogate models target aerodynamic and structural surrogacy; SHM emphasizes pattern recognition under sensor noise; and control applications optimize decision policies under dynamic uncertainty. This differentiation illustrates why no single AI method can dominate across all operational domains.

8. Future Research Directions

The trajectory of AI in floating offshore wind suggests a field entering a phase of consolidation. The proliferation of surrogate models, monitoring schemes, and digital twins demonstrates clear potential, but also exposes the boundaries of what can be achieved without deeper integration, standardization, and validation. Looking ahead, several research directions stand out as decisive for the next generation of work.

One of the most urgent needs is the creation of shared benchmark datasets. At present, nearly all reviewed studies rely on bespoke simulation campaigns, making cross-comparison nearly impossible. Establishing open repositories that combine SCADA data, laboratory tests, and validated simulation benchmarks would allow reproducible evaluation and enable more rigorous model development. Such initiatives would mirror the role of benchmark datasets in other AI-driven fields and could be coordinated by consortia of universities, classification societies, and industry partners.

Equally critical is the development of hybrid and physics-informed frameworks that retain the transparency and credibility of traditional engineering while benefiting from the adaptability of AI. Physics-informed neural networks, gray-box surrogates, and residual learning models are promising, but their application is still limited. Expanding these methods to capture complex aero-hydro-servo-elastic couplings, while embedding physical constraints, will be key for industrial acceptance and certification.

The field must also move toward safe reinforcement learning and robust control. While deep RL controllers demonstrate promise in simulations, their direct transfer to offshore operations remains risky. Future work should focus on safe RL formulations that guarantee stability, provide explainable decision rules, and allow human oversight. Embedding reinforcement learning within model predictive control or twin-in-the-loop architectures may offer pathways toward both adaptability and safety.

Digital twins will likely define the operational paradigm of floating wind farms. Current prototypes are confined to single turbines, but scaling to farm-level twins that integrate wake effects, grid interactions, and maintenance scheduling will be transformative. Achieving this vision requires advances in scalable model architectures, cloud-edge integration, and cybersecurity frameworks to ensure trust in data exchange. The credibility of twins will further depend on their ability to quantify and communicate uncertainty.

Finally, a cross-cutting research frontier lies in uncertainty-to-decision pipelines. Many models now output uncertainty intervals, but few connect them to decision-making under risk. Future work should focus on calibration metrics, probabilistic certification standards, and operational decision frameworks where uncertainty informs, rather than obstructs, planning. This is particularly urgent in the context of structural health monitoring and mooring reliability, where overconfidence in AI predictions could carry catastrophic consequences.

Equally important is bridging AI innovation with certification and operational standards. Future work should translate AI outputs—such as surrogate error, anomaly score, or RL policy reward—into measurable quantities used by certification agencies (e.g., DNV-RP-A203 [118], IEC 61400 [119]). We propose that AI-for-FOWT studies report, at minimum: (i) data provenance and uncertainty quantification, (ii) validation environment (simulation, laboratory, or field), and (iii) traceable mapping to engineering decision variables. This will facilitate regulatory acceptance and real-world deployment.

The future of AI in floating offshore wind will not be defined by any single algorithmic breakthrough. Rather, it will hinge on integration: integration of data across institutions and scales, integration of physics and learning in hybrid models, integration of safety into adaptive control, and integration of uncertainty into certification and decision-making. Only through this systemic approach can AI move from laboratory studies to robust, trusted infrastructure for the energy transition.

9. Conclusions

This review has traced the emergence of artificial intelligence as a transformative tool for floating offshore wind turbines. From early supervised learning surrogates to advanced digital twins, the field has progressed rapidly in just over a decade, with a sharp rise in publications and citations since 2020. The taxonomy we proposed shows how different families of AI methods—classical learning, deep neural surrogates, physics-informed hybrids, reinforcement learning, digital twins, and uncertainty-aware models—map onto the critical challenges of design, monitoring, control, and lifecycle management.

The bibliometric analysis revealed a global but uneven research landscape, dominated by a handful of countries and institutions, and heavily supported by public funding. Methodological assessment highlighted recurrent biases, particularly overreliance on simulation, limited field validation, and inconsistent evaluation metrics. Yet despite these limitations, the corpus demonstrates a growing recognition that AI is no longer peripheral but central to the future of floating offshore wind.

Based on the reviewed studies, the following AI approaches appear most promising for future FOWT research and development:

• − Physics-informed neural surrogates (PINNs, DeepONets): enable order-of-magnitude acceleration of coupled aero-hydro-servo-elastic simulations, provided sufficient synthetic data exist.
• − Uncertainty-aware and probabilistic encoders for structural-health monitoring: improve fault sensitivity while maintaining calibrated false-positive rates.
• − Safe reinforcement-learning controllers integrated with model-predictive control (MPC-RL): enhance energy capture and load mitigation under safety constraints.
• − Hybrid digital twins combining reduced-order physical models with online learning: support remaining-useful-life estimation and lifecycle decision-making.

These techniques are expected to mature rapidly if supported by open benchmark datasets, harmonized evaluation metrics, and ‘twin-in-the-loop’ validation campaigns.

Looking forward, the field must pivot toward integration: shared benchmarks for reproducibility, hybrid models that balance physics and learning, safe and interpretable reinforcement learning for control, and digital twins that scale from turbines to farms. Most critically, uncertainty must be connected to decision-making frameworks to support certification and operational trust. Only then will AI move beyond proof-of-concept into the reliable, industry-ready infrastructure required for the global energy transition.