Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically rele...
In metal additive manufacturing (AM), precise temperature field prediction is crucial for process monitoring, automation, control, and optimization. Traditional methods, primarily offline and data-driven, struggle with adapting to real-time changes a...
Computational modeling and machine learning have impacted several different areas of science and accelerated advancements in multiple venues. Yet traditional machine learning models have many well-known drawbacks: besides demanding a significant amou...
Physics-informed machine learning (PIML) has gained significant attention in various scientific fields and is now emerging in the area of tribology. By integrating physics-based knowledge into machine learning models, PIML offers a powerful tool for...
Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even...
Physics-Informed Machine Learning has emerged as a powerful paradigm for fatigue and fracture analysis, combining the data-driven flexibility of neural networks with the consistency of physical laws. This scoping review maps the extent, range, and na...
Accurate state estimation for quadrotors under wind-induced disturbances remains a critical challenge in dynamic outdoor environments. Existing model-based and data-driven approaches often struggle with real-time adaptation and catastrophic forgettin...
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks...
Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based su...
Bridge health monitoring is crucial for ensuring the safety and durability of infrastructure. In traditional methods, physics-based models have high interpretability but are difficult to handle complex nonlinear problems, while purely data-driven mac...
This study proposes an enhanced physics-informed neural network (PINN) framework for predicting seepage fields around deeply buried tunnels with limited field measurements. Hard-constrained boundary enforcement via distance-function trial functions i...
This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean field games (MFGs). The MFG system incorporates a forward PDE to model the propagation of po...
Deep learning has improved precipitation estimation and forecasting, but high predictive skill does not by itself ensure physically credible behavior, robustness under distribution shift, or reliable uncertainty. This critical narrative review examin...
Biochar water treatment stands at a decisive crossroads, where the promise of large-scale application meets the reality of laboratory trial-and-error. This study contends that the fundamental bottleneck to progress lies in the field’s persisten...
DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed over the past decade. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the da...
Machine Learning (ML)-based methods have been identified as capable of providing up to one day ahead Photovoltaic (PV) power forecasts. In this research, we introduce a generic physical model of a PV system into ML predictors to forecast from one to...
The mechanical performance of secondary aluminum alloys depends on Secondary Dendrite Arm Spacing (SDAS). Commercial casting simulations accurately predict local thermal history but typically neglect the influence of compositional variability on SDAS...
Data-driven aeration optimization is an effective approach for reducing energy consumption in wastewater treatment plants (WWTPs). However, in information-limited scenarios, newly established or emerging-market WWTPs often lack historical labels for...
Fused filament fabrication (FFF), an additive manufacturing process, is an emerging technology with issues in the uncertainty of mechanical properties and quality of printed parts. The consideration of all main and interaction effects when changing p...
Fast charging of lithium-ion batteries requires balancing charging efficiency with electrochemical safety to minimize degradation and lithium plating. Conventional charging strategies and existing reinforcement learning approaches often lack physical...
Quantitative determination of gangue content is important for efficient coal use and intelligent coal–gangue separation. We combine transmission terahertz time-domain spectroscopy (THz-TDS), multidomain feature fusion, and machine learning to p...
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...
Nitrogen control in basic oxygen furnace (BOF) steelmaking is critical, as dissolved nitrogen concentrations exceeding 30–40 ppm detrimentally affect the mechanical properties and formability of low-carbon steel products; however, no prior stud...
This study focuses on reducing harmonic distortion in residential electrical systems caused by the use of nonlinear household appliances. A combined prediction and control framework is proposed to estimate and reduce total harmonic distortion (THD) u...
The fatigue life prediction of additively manufactured (AM) alloys remains challenging owing to process-induced defects, microstructural variability, and complex loading conditions of the alloys. This study presents a domain-knowledge-informed machin...
In 3D integrated circuits, a placement decision that looks acceptable from a 2D wirelength view can still create a local thermal or stress problem after stacking. This issue becomes more visible as the number of tiers and the density of vertical inte...
With the rapid development of artificial intelligence technology, DRL has shown great potential in solving complex real-time optimal power flow problems of modern power systems. Nevertheless, traditional DRL methodologies confront dual bottlenecks: (...
High renewable penetration and large-scale green hydrogen production are accelerating the formation of the new-type power system (NTPS), in which electrical dispatch, electrolysis, hydrogen storage, fuel-cell reconversion, and flexible demand must be...
Predicting the Remaining Useful Life (RUL) of engine oil is critical for proactive maintenance and fleet reliability. However, irregular and noisy single-point sampling presents challenges for conventional prognostic models. To address this, a hierar...
Low-altitude radar target recognition is important for intelligent airspace monitoring, unmanned aerial vehicle (UAV) supervision, airport bird-strike prevention, and low-altitude remote sensing. Reliable recognition remains difficult because birds,...
The accurate determination of nuclear level density (NLD) is essential for a wide range of applications in nuclear science, including reactor design, nuclear astrophysics, and nuclear data evaluation. Traditional phenomenological models often face ch...
Diverse application scenarios demand frequency-selective surfaces (FSSs) with tailored center frequencies and bandwidths. However, their design traditionally relies on iterative full-wave simulations using tools such as the High-Frequency Structure S...
Drying is a complex process of simultaneous heat, mass, and momentum transport phenomena with continuous phase changes. Numerical modelling is one of the most effective tools to mechanistically express the different physics of drying processes for ac...
Mean-field games (MFGs) are developed to model the decision-making processes of a large number of interacting agents in multi-agent systems. This paper studies mean-field games on graphs (
The development of filled photopolymer composites for Digital Light Processing (DLP) additive manufacturing requires optimizing processing parameters to achieve the desired mechanical properties. Traditional experimental approaches are time-intensive...
Machine learning (ML), one of the AI techniques, has been used in geotechnical engineering for over three decades, resulting in more than 600 peer-reviewed papers. However, AI applications in geotechnical engineering are significantly lagging compare...
The infiltration of water into the soil can lead to slope instability, which is one of the important causes of many geological hazards (such as landslides and debris flows). Therefore, the numerical investigation of the soil–water infiltration...
As modern systems become more complex, their control strategy no longer relies only on measurement data from probes; it also requires information from mathematical models for non-measurable places. On the other hand, those mathematical models can lea...
Triply periodic minimal surface (TPMS) structures provide high surface area to volume ratios and tunable conduction pathways, but predicting their thermal behavior across different metallic materials remains challenging because multi-material experim...
Carburizing process optimization requires accurate prediction of multiple interrelated outcomes, yet existing models either oversimplify the physics or require prohibitively large datasets. Here, we present a physics-informed machine learning (PIML)...
This study proposes a supervised machine learning framework for vibration-based fault diagnosis of rotating machinery using integrated data-driven and physics-informed feature sets. A dataset acquired under variable load and multiple operating condit...
Estimating lithium-ion battery state of health (SOH) across operating conditions is challenging because operating profiles, measurement characteristics, and degradation trajectories differ among domains. This study proposes a physics-informed transfe...
Electromagnetic scattering from electronic platforms degrades system performance, increases radar detectability, and intensifies electromagnetic interference in modern radar and communication systems. Electromagnetic absorbing layers offer an effecti...
Power system state estimation (PSSE) requires accurate and timely inference from noisy measurements, but large labeled operational datasets are often unavailable and deployment data may differ from offline training data. This paper presents Deep Tran...
This study aimed to develop and validate a physics-informed neural network (PINN) framework for data-efficient and physically consistent process optimization in the laser powder bed fusion (LPBF) of Inconel 718 (IN718) superalloy. Laser powder bed fu...
Topology optimization of electromagnetic devices is computationally formidable owing to the combinatorial on/off material distribution on unstructured finite element meshes and repeated Maxwell equation solves. This paper develops GPRL-TO, a data-dri...
Accurate prediction of concrete compressive strength remains a challenging problem due to the complex and nonlinear interactions among mixture components and curing conditions. While machine learning approaches have shown promising results, existing...
As the annual reduction factors of the International Maritime Organization (IMO) Carbon Intensity Indicator (CII) regulation have progressively tightened, predicting ship fuel consumption from operating conditions before a voyage has become essential...
High-altitude isolated power systems characterized by hybrid run-of-river hydropower and distributed solar photovoltaic installations face severe operational instability due to localized microclimatic volatility. Managing this instability is fundamen...
The prediction of metal fatigue life has evolved from classical empirical approaches to advanced, data-driven computational models. However, traditional methods struggle with large data scatter, complex variable-amplitude loading, and the cost of exp...
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