Accurate orchard-scale soil moisture information is a key requirement for efficient irrigation management in perennial crops such as citrus orchards, particularly in Mediterranean environments characterized by water scarcity and strong spatial and te...
Accurate estimation of surface evapotranspiration (ET) in the Heihe River Basin using remote sensing data is crucial for understanding water dynamics in arid regions. In this paper, by coupling physical constraints and machine learning for hybrid mod...
Accurate measurements of global horizontal irradiance (GHI) are fundamental for solar energy assessment. However, the cost and deployment constraints of standard pyranometers limit their widespread use. This work presents a low-cost pseudo-pyranomete...
The issue of water resources in a semi-arid country such as Morocco has been present for many years and is becoming increasingly critical. The droughts experienced over recent decades have demonstrated the country’s extreme vulnerability to any...
This study proposes and evaluates a hybrid framework for predicting curling stone motion by combining physical models with machine learning. Motion capture data from a curling sheet were used to train modules for sliding trajectory prediction and pos...
The use of digital twin and shadow concepts for industrial material processes has introduced new approaches to bridge the gap between physical and cyber manufacturing processes. Consequently, many multidisciplinary areas, such as advanced sensor tech...
This review surveys recent progress in hybrid artificial intelligence (AI) approaches for gas turbine intelligent digital twins, with an emphasis on models that integrate physics-based simulations and machine learning. The main contribution is the in...
2 December 2021
In this paper, an innovative hybrid modelling technique based on machine learning and building dynamic simulation is presented for the prediction of indoor thermal comfort feedback from occupants in an office building in Le Bourget-du-Lac, Chamb&eacu...
Predicting the fatigue lifespan of Twisted String Actuators (TSAs) is essential for improving the reliability of robotic and mechanical systems that rely on flexible transmission mechanisms. Traditional empirical approaches based on regression or Wei...
Advanced patterning technologies have evolved from direct optical image transfer into predictive manufacturing frameworks in which defects are shaped by imaging physics, material response, process variation, and computational correction. Starting fro...
In this study, a novel “Physics-Informed Hybrid Machine Learning” framework was developed to model and optimize the complex combustion and carbon-based emission characteristics of Cu2O nano-additive doped diesel fuel. To reduce reliance o...
State estimation is a fundamental component of model-based Fault Detection and Diagnosis (FDD) in dynamic systems, underpinning real-time monitoring, predictive maintenance, and safety-critical operations across industries such as aerospace, power sy...
Accurate prediction of soil–structure interface shear strength (τmax) is critical for reliable geotechnical design. This study combines experimental testing with interpretable machine learning to overcome the limitations of traditional empi...
The estimation of indoor thermal comfort and the associated occupant feedback in office buildings is important to provide satisfactory and safe working environments, enhance the productivity of personnel, and to reduce complaints. The assessment of t...
Droughts, intensified by climate change and human activities, pose a significant threat to winter wheat cultivation in the Huang-Huai-Hai (HHH) region. Soil moisture drought indices are crucial for monitoring agricultural droughts, while challenges s...
The rise of machine learning (ML) has significantly advanced the field of coastal oceanography. This review aims to examine the existing deficiencies in numerical predictions of storm surges and the effort that has been made to improve the predictive...
An accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal be...
Machine learning (ML) has emerged as a transformative tool in geotechnical engineering, offering data-driven alternatives to traditional empirical and analytical approaches. This paper presents a critical narrative review of recent advances in theory...
Background: Almaty, located in a mountain–valley basin, frequently experiences stagnant conditions that trap pollutants and cause sharp diurnal contrasts in air quality. Current forecasting systems either offer detailed physical realism at high...
Rainfall–triggered clustered landslides pose severe risks to communities and infrastructure in mountainous regions. High–precision susceptibility assessment is essential for early warning and hazard mitigation. The traditional buffering m...
Accurate health assessment is essential for the reliable and cost-effective operation of lithium-ion battery energy storage systems. This study presents a physics-informed explainable machine-learning framework for hybrid State-of-Health (SoH) estima...
Accurate estimation of residential building energy consumption and associated CO2 emissions is essential for refined urban carbon management. This study develops a hybrid framework that integrates physics-based simulation and machine learning to esti...
Hyperspectral (HSI) remote sensing has emerged as a transformative technology for Earth Observation, enabling detailed assessments across different domains. The current PRISMA-aligned systematic review aims to compare classical physics-based algorith...
Soilless cultivation and integrated water–fertilizer systems rely on precise control of electrical conductivity (EC) in nutrient solutions. Although EC can be measured online, predicting EC from known ionic composition is valuable for offline f...
Tool wear is critical to quality, productivity, and sustainability in manufacturing processes. Therefore, accurately monitoring and predicting wear is one of the primary goals of smart manufacturing systems. While AI-based approaches have achieved si...
Fatigue has always been one of the major causes of structural failure, where repeated loading and unloading cycles reduce the fracture energy of the material, causing it to fail at stresses lower than its monotonic strength. However, predicting fatig...
The efficient development of low-permeability reservoirs is critically constrained by severe geological heterogeneity, marginal permeability (<10 mD), and the consequent prevalence of low-productivity wells. Conventional intermittent production ma...
PET scans have long been used in oncology imaging to provide molecular and metabolic information about diseases. The use of artificial intelligence (AI) in PET scans in oncology theranostics has the potential to optimise PET modality and overcome the...
Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for global ocean wind monitoring with high temporal resolution. However, accurate wind speed retrieval remains challenging due to the complex scattering mechanism...
To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar time series, seeking to improve forecasting accuracy by including neighboring solar information. This review work is, to the authors’ understandin...
Reliable digital twins of complex nonlinear systems require not only high predictive accuracy but also physical consistency, robustness under degraded operating conditions, and explicit uncertainty handling. Purely data-driven models often suffer fro...
Structural health monitoring (SHM) plays a critical role in the early identification and assessment of structural damage, thereby enhancing the safety and reliability of civil infrastructure. Structural damage identification generally encompasses thr...
The paper presents the results of research on the hybrid industrial tomograph electrical impedance tomography (EIT) and ultrasonic tomography (UST) (EIT-UST), operating on the basis of electrical and ultrasonic data. The emphasis of the research was...
In this work, we developed a data-driven framework to predict near-surface (0–5 cm) soil moisture (SM) by mapping inputs from the Soil & Water Assessment Tool to SM time series from NASA’s Soil Moisture Active Passive (SMAP) satellite...
This paper provides a systematic synthesis of recent developments in physics-informed neural networks (PINNs) applied to aerospace engineering, with an emphasis on their role in physically consistent surrogate modeling, forward simulation, and invers...
This study presents an AI-based framework that unifies civil and mechanical engineering principles to optimize the structural performance of steel frameworks. Unlike traditional methods that analyze material behavior, load-bearing capacity, and dynam...
The convergence of Lean manufacturing and Industry 4.0 requires digital infrastructures capable of transforming high-frequency telemetry into actionable insights. However, architectures that integrate near real-time data with closed-loop process cont...
Lithium iron phosphate batteries have seen a recent rise in usage in electric vehicles and battery energy storage systems. For these applications, reliability is of paramount importance, influences long-term adoption and high return on investment, es...
The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further e...
Automatic Machine Learning (AML) methods are currently considered of great interest for use in the development of cyber-physical systems. However, in practice, they present serious application problems with respect to fitness computation, overfitting...
In some real-world problems, machine learning is faced with little data due to limited resources such as sensors, time, and budget. In this case, the conventional machine learning approach may fail or perform badly. To develop a well-functioning mode...
The objective of this study was to evaluate the capability of machine learning models to accurately predict complex near-critical phase behavior in CO2–hydrocarbon systems, which are crucial for enhanced oil recovery and carbon storage applicat...
Machine learning has been used in hydrological applications for decades, and recently, it was proven to be more efficient than sophisticated physically based modelling techniques. In addition, it has been used in hybrid frameworks that combine hydrol...
The increasing penetration of photovoltaic solar energy has intensified the need for accurate production forecasting to ensure efficient grid operation. This study presents a critical comparison of traditional statistical methods and machine learning...
River flood routing computes changes in the shape of a flood wave over time as it travels downstream along a river. Conventional flood routing models, especially hydrodynamic models, require a high quality and quantity of input data, such as measured...
A hybrid modeling framework for predicting the mechanical properties of Al-Mg-Si alloys, that blends physics-based and machine-learning models, is developed and tested. Motivated by a demand for post-consumer material (PCM) content in wrought alumini...
Surface drifter trajectory prediction is essential for applications in environmental management, maritime safety, and climate studies. This survey paper reviews research from the past two decades, and systematically classifies the evolution of method...
Constructing accurate Potential Energy Surfaces (PES) is a central task in molecular modeling, as it determines the forces governing nuclear motion and enables reliable quantum dynamics simulations. While ab initio methods can provide accurate PES, t...
This paper evaluates the ability of machine learning (ML) algorithms to capture and reproduce complex multiphase behavior in surfactant–oil–water systems. The main objective of the paper is to evaluate the ability of machine learning algo...
Machine learning (ML) methods present a valuable opportunity for modeling the non-linear behavior of the injection molding process. They have the potential to predict how various process and material parameters affect the quality of the resulting par...
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