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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 151–192 of 192 papers

TitleStatusHype
A physics-informed machine learning model for reconstruction of dynamic loads—0
A Physics-informed machine learning model for time-dependent wave runup prediction—0
Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems—0
A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection—0
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers—0
Breaking the Diffraction Barrier for Passive Sources: Parameter-Decoupled Superresolution Assisted by Physics-Informed Machine Learning—0
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations—0
Calibrating constitutive models with full-field data via physics informed neural networks—0
Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations—0
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach—0
DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training—0
Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning—0
Discovering nonlinear resonances through physics-informed machine learning—0
Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning—0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
Empirical modeling and hybrid machine learning framework for nucleate pool boiling on microchannel structured surfaces—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations—0
Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework—0
FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—0
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning—0
Further Exploration of Precise Binding Energies from Physics Informed Machine Learning and the Development of a Practical Ensemble Model—0
Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks—0
Grey-box models for wave loading prediction—0
h-analysis and data-parallel physics-informed neural networks—0
How important are activation functions in regression and classification? A survey, performance comparison, and future directions—0
Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning—0
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise—0
Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks—0
KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics—0
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes—0
(Un)supervised Learning of Maximal Lyapunov Functions—0
Learning ergodic averages in chaotic systems—0
Machine Learning with Physics Knowledge for Prediction: A Survey—0
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning—0
Towards Model Reduction for Power System Transients with Physics-Informed PDE—0
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models—0
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives—0
NETWORK COMPRESSION FOR MACHINE-LEARNT FLUID SIMULATIONS—0
Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics—0
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