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Physics-informed machine learning

Machine learning used to represent physics-based and/or engineering models

Papers

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Showing 51–100 of 192 papers

TitleStatusHype
Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models—0
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems—0
Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks—0
Understanding Generalization in Physics Informed Models through Affine Variety Dimensions—0
PDE-DKL: PDE-constrained deep kernel learning in high dimensionalityCode0
Empirical modeling and hybrid machine learning framework for nucleate pool boiling on microchannel structured surfaces—0
Physics-Informed Machine Learning for Efficient Reconfigurable Intelligent Surface Design—0
Physics-Informed Machine Learning for Microscale Drying of Plant-Based Foods: A Systematic Review of Computational Models and Experimental Insights—0
KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics—0
Towards Physically Interpretable World Models: Meaningful Weakly Supervised Representations for Visual Trajectory Prediction—0
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series AnalysisCode0
RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity FactorsCode0
Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin ModelsCode0
Physics-informed Machine Learning for Battery Pack Thermal Management—0
Advancing Hybrid Quantum Neural Network for Alternative Current Optimal Power Flow—0
A Data-driven Crowd Simulation Framework Integrating Physics-informed Machine Learning with Navigation Potential Fields—0
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning—0
Transcriptome and Redox Proteome Reveal Temporal Scales of Carbon Metabolism Regulation in Model Cyanobacteria Under Light Disturbance—0
Structural Constraints for Physics-augmented Learning—0
DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training—0
Non-overlapping, Schwarz-type Domain Decomposition Method for Physics and Equality Constrained Artificial Neural NetworksCode0
Physics-informed kernel learningCode0
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations—0
State-space models are accurate and efficient neural operators for dynamical systemsCode0
(Un)supervised Learning of Maximal Lyapunov Functions—0
Spectrally Informed Learning of Fluid Flows—0
Machine Learning with Physics Knowledge for Prediction: A Survey—0
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise—0
Physics-Informed Machine Learning for Grade Prediction in Froth Flotation—0
Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling—0
Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks—0
Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine LearningCode0
Physical Data Embedding for Memory Efficient AI—0
Deep Learning Evidence for Global Optimality of Gerver's SofaCode0
Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain HyperelasticityCode0
Physics-Informed Machine Learning for Smart Additive Manufacturing—0
Predicting 3D Rigid Body Dynamics with Deep Residual Network—0
Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator—0
Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning—0
Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines—0
Differentiable Predictive Control for Large-Scale Urban Road NetworksCode0
Adapting Physics-Informed Neural Networks to Improve ODE Optimization in Mosquito Population DynamicsCode0
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks—0
Physics-Informed Machine Learning On Polar Ice: A Survey—0
Physics-informed Discretization-independent Deep Compositional Operator NetworkCode0
Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations—0
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes—0
Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming abilityCode0
Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures—0
Physics-Informed Machine Learning for the Inverse Design of Wave Scattering Clusters—0
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