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physics-informed machine learning model for prediction of ground reflected wave peak overpressure

Defence Technology（防务技术） 2024年第11期41卷 119-133页

作者： Haoyu Zhang Yuxin Xu Lihan Xiao Canjie Zhen State Key Lab of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China Chongqing Innovation Center Beijing Institute of TechnologyChongqing 404100China Tangshan Research Institute Beijing Institute of TechnologyTangshan 442000China Shandong Special Industry Group Co.Ltd. Zibo 255000China

The accurate prediction of peak overpressure of explosion shockwaves is significant in fields such as explosion hazard assessment and structural protection, where explosion shockwaves serve as typical destructive elements. Aiming at the problem of insufficient accuracy of the existing physical models for predicting the peak overpressure of ground reflected waves, two physics-informed machine learning models are constructed. The results demonstrate that the machine learning models, which incorporate physical information by predicting the deviation between the physical model and actual values and adding a physical loss term in the loss function, can accurately predict both the training and out-oftraining dataset. Compared to existing physical models, the average relative error in the predicted training domain is reduced from 17.459%-48.588% to 2%, and the proportion of average relative error less than 20% increased from 0% to 59.4% to more than 99%. In addition, the relative average error outside the prediction training set range is reduced from 14.496%-29.389% to 5%, and the proportion of relative average error less than 20% increased from 0% to 71.39% to more than 99%. The inclusion of a physical loss term enforcing monotonicity in the loss function effectively improves the extrapolation performance of machine learning. The findings of this study provide valuable reference for explosion hazard assessment and anti-explosion structural design in various fields.

关键词： Blast shock wave Peak overpressure machine learning physics-informed machine learning

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physics-informed machine learning for solar-thermal power systems

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ENERGY CONVERSION AND MANAGEMENT 2025年 327卷

作者： Osorio, Julian D. De Florio, Mario Hovsapian, Rob Chryssostomidis, Chrys Karniadakis, George Em Natl Renewable Energy Lab Ctr Energy Convers & Storage Syst Golden CO 80401 USA Brown Univ Div Appl Math Providence RI 02912 USA Natl Renewable Energy Lab Energy Syst Integrat Golden CO 80401 USA MIT Dept Mech Engn Cambridge MA 02139 USA

Thermal energy system modeling traditionally relies on experimental correlations to estimate heat transfer coefficients using dimensionless numbers and thermophysical properties. Multiple correlations have been proposed for different flow regimes, system geometries, and boundary conditions;however, despite their widespread use, these correlations present significant limitations related to accuracy, valid flow regime ranges, and adaptability to complex system geometries. Moreover, systems powered by intermittent renewable sources, such as solar, face added challenges due to high fluctuations in heat transfer coefficients and system variables. In this work, we change fundamentally this old paradigm. We demonstrate an experimentally validated, physics-informed machine learning approach to accurately estimate time-dependent heat transfer coefficients in solar-thermal energy systems with high variability in energy input, boundary conditions, and load. This method combines data with governing physics, providing a more accurate modeling alternative to purely data-driven frameworks and addressing the limitations of traditional experimental correlations and the need for extensive historical data for offline training. More broadly, the proposed paradigm shift can be employed in other energy plants and complex systems-of-systems.

关键词： physics-informed machine learning Extreme theory of functional connections Solar-thermal power systems Heat transfer coefficients prediction Transient system operation

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physics-informed machine learning for system reliability analysis and design with partially observed information

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 254卷

作者： Xu, Yanwen Bansal, Parth Wang, Pingfeng Li, Yumeng Univ Texas Dallas Dept Mech Engn Richardson TX 75080 USA Univ Illinois Urbana & Champaign Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA

Constructing a high-fidelity predictive model is crucial for analyzing complex systems, optimizing system design, and enhancing system reliability. Although Gaussian Process (GP) models are well-known for their capability to quantify uncertainty, they rely heavily on data and necessitate a large representative dataset to establish a high-fidelity predictive model. physics-informed machine learning (PIML) has emerged as a way to integrate prior physics knowledge and machine learning models. However, current PIML methods are generally based on fully observed datasets and mainly suffer from two challenges: (1) effectively utilize partially available information from multiple sources of varying dimensions and fidelity;(2) incorporate physics knowledge while maintaining the mathematical properties of the GP-based model and uncertainty quantification capability of the predictive model. To overcome these limitations, this paper proposes a novel physics-informed machine learning method that incorporates physics prior knowledge and multi-source data by leveraging latent variables through the Bayesian framework. This method effectively utilizes partially available limited information, significantly reduces the need for costly fully observed data, and improves prediction accuracy while maintaining the model property of uncertainty quantification. The developed approach has been demonstrated with two case studies: the vehicle design problem and the battery capacity loss prediction. The case study results demonstrate the effectiveness of the proposed model in complex system design and optimization while propagating uncertainty with limited fully observed data.

关键词： physics-informed machine learning Partially observed information Uncertainty quantification Bayesian inference Battery capacity estimation Uncertainty propagation Multi-fidelity data fusion

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physics-informed machine learning for accurate SOH estimation of lithium-ion batteries considering various temperatures and operating conditions

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ENERGY 2025年 318卷

作者： Lin, Chunsong Tuo, Xianguo Wu, Longxing Zhang, Guiyu Lyu, Zhiqiang Zeng, Xiangling Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Anhui Sci & Technol Univ Coll Mech Engn Chuzhou 233100 Peoples R China Sichuan Univ Sci & Engn Sch Mech Engn Yibin 64400 Peoples R China Anhui Univ Sch Internet Hefei 230039 Anhui Peoples R China

Accurate State of Health (SOH) estimation for lithium batteries (LIBs) is crucial for the safe operation of battery systems. However, the lack of physical properties and the varied operating conditions in real-world use further increase the difficulty of traditional SOH estimation, making it a significant challenge in current research. For this reason, this paper proposes a physics-informed machine learning (PIML) method for accurate SOH estimation of LIBs varied operating conditions. Considering the fully charged relaxation voltage data obtained easily in practical applications, firstly, this paper discussed the relaxation voltage data related to the battery's aging characteristics from the experimental tests. Secondly, the fractional-order equivalent circuit model (FOECM) is constructed and parameters characterizing battery degradation are identified for extracting the physical features. Ultimately, a novel PIML framework based FOECM of LIB is developed, then the datasets of three different battery types under different temperatures and discharge rates are used and validated for SOH estimation without considering any usage information. Experimental results show that the PIML method proposed in this paper can quickly achieve SOH estimation and keep the accuracy in 0.84 % for different types of batteries under varying experimental conditions. In addition, compared with other feature extraction methods, the PIML-based SOH estimation has obvious advantages with 16.2 %, which provides an important reference for the design and optimization of advanced battery management systems.

关键词： Lithium-ion battery physics-informed machine learning State of health Voltage relaxation Battery management systems

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physics-informed machine learning for forecasting power exchanges at the interface between transmission and distribution systems

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 238卷

作者： Rosseel, Arnaud Zad, Bashir Bakhshideh Vallee, Francois De Greve, Zacharie Univ Mons Power Syst & Markets Res Grp Mons Belgium 31 Blvd Dolez B-7000 Mons Belgium

Power exchanges at Transmission-Distribution interfaces are crucial for both the Transmission System Operators (TSOs) and the Distribution System Operators (DSOs). In the past, simple hypothesis as a constant power factor sufficed for characterizing distribution networks and predicting power flows at Transmission- Distribution interfaces. However, the growing integration of distributed energy resources has led to an increased volatility in both active and reactive power flows, rendering traditional models less effective. This study presents a novel physics-informed machine learning (PIML) model designed to enhance the prediction of power exchanges at Transmission-Distribution interfaces. A novelty of the model lies in its combination of an Inverse Load Flow formulation, which defines an equivalent model of the distribution network (by calculating equivalent resistance and reactance using load flow equations), with classical data-driven regression techniques. Simulation results conducted on a modified version of the Oberrhein MV network highlight the superiority of the proposed PIML approach in front of full ML based methods, as demonstrated by a statistical indicator and an application-oriented evaluation. In addition, this research adopts the TSO perspective through a 2-step Optimal Power Flow analysis that integrates interface power predictions and enables the calculation of production and deviation costs. This multifaceted approach provides valuable insights into the practical implications of the power prediction accuracy on the TSO decision-making process and underscores the significance of accurate power exchange forecasts in the evolving electricity landscape.

关键词： Transmission network Distribution network Short-term forecasting physics-informed machine learning

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physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods

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ENERGY STORAGE MATERIALS 2024年 68卷

作者： Navidi, Sina Thelen, Adam Li, Tingkai Hu, Chao Univ Connecticut Dept Mech Engn Storrs CT 06269 USA Iowa State Univ Dept Mech Engn Ames IA 50011 USA

Monitoring the health of lithium-ion batteries' internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typically involves aging many cells and destructively analyzing them throughout the aging test, limiting the scope of quantifiable degradation to the test conditions and duration. Fortunately, recent advances in physics-informed machine learning (PIML) for modeling and predicting the battery state of health demonstrate the feasibility of building models to predict the long-term degradation of a lithium-ion battery cell's major components using only shortterm aging test data by leveraging physics. In this paper, we present four approaches for building physicsinformed machine learning models and comprehensively compare them, considering accuracy, complexity, ease-of-implementation, and their ability to extrapolate to untested conditions. We delve into the details of each physics-informed machine learning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantable-grade lithium-ion cells subjected to varying temperatures and C-rates over four years. This paper aims to facilitate the selection of an appropriate physics-informed machine learning method for predicting long-term degradation in lithium-ion batteries, using short-term aging data while also providing insights about when to choose which method for general predictive purposes.

关键词： Lithium-ion battery Degradation diagnostics Half-cell model physics-informed machine learning Comparative study

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physics-informed machine learning with Data-Driven Equations for Predicting Organic Solar Cell Performance

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ACS APPLIED MATERIALS & INTERFACES 2024年第42期16卷 57467-57480页

作者： Khatua, Rudranarayan Das, Bibhas Mondal, Anirban Indian Inst Technol Gandhinagar Dept Chem Gandhinagar 382355 Gujarat India

Organic solar cells (OSCs) have emerged as a promising solution in pursuing sustainable energy. This study presents a comprehensive approach to advancing OSC development by integrating data-driven equations from quantum mechanical (QM) descriptors with physics-informed machine learning (PIML) models. We circumvent traditional experimental limitations through high-throughput QM calculations, prioritizing transparent and interpretable models. Using the SISSO++ method, we identified key descriptors that effectively map the relationships between input variables and photovoltaic performance metrics. Our innovative predictive models, derived from SISSO outputs, excel in forecasting critical OSC parameters such as short-circuit current (J(SC)), open-circuit voltage (V-OC), fill factor (FF), and power conversion efficiency (PCEmax), achieving high accuracy even with limited data sets. To validate our models' practical utility, we applied the PIML framework to a newly compiled data set of OSC devices, demonstrating their versatility and capability in pinpointing high-performance materials. This research underscores the strong predictive power of our models, bridging the gap between experimental results and theoretical predictions and making significant contributions to the advancement of sustainable energy technologies.

关键词： organic solar cells physics-informed machine learning sustainable energy technology quantum mechanics

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physics-informed machine learning for reservoir management of enhanced geothermal systems

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 234卷

作者： Yan, Bicheng Xu, Zhen Gudala, Manojkumar Tariq, Zeeshan Sun, Shuyu Finkbeiner, Thomas King Abdullah Univ Sci & Technol KAUST Phys Sci & Engn PSE Div Thuwal 239556900 Saudi Arabia

With the energy demand arising globally, geothermal recovery by Enhanced Geothermal Systems (EGS) becomes a promising option to bring sustainable energy supply along with mitigating CO2 emission. However, reservoir management of EGS primarily relies on reservoir simulation, which is quite expensive due to the reservoir heterogeneity, the interaction of matrix and fractures, and the intrinsic multi-physics coupled nature. Therefore, a robust optimization framework is critical for the management of EGS. We develop a general physics-informed machine learning (PIML) framework for reservoir management with multiple optimization options. A robust forward surrogate model fl is developed based on a convolutional neural network, and it successfully learns the nonlinear relationship between input reservoir model parameters (e.g., fracture permeability field) and interested state variables (e.g., temperature field and produced fluid temperature). fl is trained using simulation data from the EGS coupled thermal-hydro simulation model by sampling reservoir model parameters. As fl is accurate, efficient and fully differentiable, EGS thermal efficiency can be optimized following two schemes: (1) training a control network fc to map reservoir geological parameters to reservoir decision parameters by coupling it with fl;(2) directly optimizing the reservoir decision parameters based on coupling the existing optimizers with fl. We evaluate the impact of reservoir model parameters on the thermal recovery based on simulation datasets through sensitivity analyses, and demonstrated that injection mass rate dominates thermal recovery. Further, the forward model fl performs accurate and stable predictions of evolving temperature fields (relative error 1.27 +/- 0.89%) in EGS and the time series of produced fluid temperature (relative error 0.26 +/- 0.46%), and its speedup to the counterpart high-fidelity simulator is 4564 times. When optimizing with fc, we achieve thermal recovery with a re

关键词： physics-informed machine learning Reservoir management Optimization Well control optimization Enhanced geothermal systems Fracture

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physics-informed machine learning for noniterative optimization in geothermal energy recovery

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APPLIED ENERGY 2024年 365卷

作者： Yan, Bicheng Gudala, Manojkumar Hoteit, Hussein Sun, Shuyu Wang, Wendong Jiang, Liangliang King Abdullah Univ Sci & Technol KAUST Phys Sci & Engn PSE Div Thuwal 239556900 Saudi Arabia China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China Univ Calgary Dept Chem & Petr Engn Calgary AB Canada

Geothermal energy is clean, renewable, and cost-effective and its efficient recovery management mandates optimizing engineering parameters while considering the underpinning physics, typically achieved through computationally intensive simulators. This study proposes a novel physics -informed machine learning (PIML) framework for geothermal reservoir optimization, integrating a data wrangler to process high-fidelity simulations, a forward network for forward predictions, and a control network to optimize engineering decision parameters while maximizing the objective function and satisfying various engineering constraints. The PIML incorporates an improved Hyperbolic-ReLU (HyperReLU) model to predict the produced geothermal fluid temperature robustly. The forward model uses a neural network to predict hyper -parameters of HyperReLU from reservoir model input and estimates the produced fluid temperature and energy. Further, the control network is trained with labels automatically generated by the forward model. During prediction, it can infer optimum decision parameters noniteratively by inputting uncertain reservoir parameters, ensuring it maximizes the objective function. Numerical experiments reveal that the HyperReLU enhances long-term predictive stability, and the forward network can achieve predictions of the produced temperature and energy within errors of 0 . 53 +/- 0 . 46% and 0 . 60 +/- 0 . 74%, respectively. We examine PIML to control the produced temperature drops or maximize the total energy recovery. Compared to the differential evolution (DE) optimizer, PIML closely matches DE with a 53.7% increase in total energy while running 5,465 times faster than DE. Moreover, PIML presents great efficiency and accuracy and is scalable for field-scale geothermal well-control design and other similar optimization problems.

关键词： physics-informed machine learning Noniterative optimization Unsupervised learning Geothermal Reservoir management

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physics-informed machine learning for Na-Ion conductivity and activation energy

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JOURNAL OF NON-CRYSTALLINE SOLIDS 2025年 657卷

作者： Mandal, Indrajeet Mannan, Sajid Lu, Yuanqing Gosvami, Nitya Nand Wondraczek, Lothar Krishnan, N. M. Anoop Indian Inst Technol Delhi Sch Interdisciplinary Res New Delhi 110016 India Indian Inst Technol Delhi Dept Civil Engn New Delhi 110016 India Univ Jena Otto Schott Inst Mat Res D-07743 Jena Germany Univ Jena Ctr Energy & Environm Chem CEEC Jena D-07743 Jena Germany Indian Inst Technol Delhi Dept Mat Sci & Engn New Delhi 110016 India Indian Inst Technol Delhi Yardi Sch Artificial Intelligence New Delhi 110016 India

Glass-based electrolytes are promising for solid-state batteries due to the absence of grain boundaries. However, filtering the compositional space for suitable glass formulations is challenging due to the extremely wide compositional space of multicomponent glasses. Here, we consider a highly consistent dataset of sodium-ion conductive glasses for developing a physics-informed machine learning model (PIML) of ion conductivity and transport activation energy. The PIML achieves a remarkable 99% goodness of fit for conductivity, outperforming classical ML models. Moreover, it predicts conductivity (sigma), activation energy (Ea), and pre-exponential factor (sigma 0) at various temperatures, even for compositions beyond the training data. Incorporating the Arrhenius equation as a hard constraint enables physically meaningful predictions, especially at varying temperatures. Interpreting the PIML model using Shapley additive explanations (SHAP) highlights oxygen's substantial impact on Ea. Aside from providing progress in the specific field of glass-based solid electrolytes, these findings highlight the ongoing importance of consistent experimental campaigns for high-quality ML predictions.

关键词： Glass Na-ion conductivity ML physics-informed machine learning

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