physics-informed learning

Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks ICLR 2022 Physics Informed Deep Kernel Learning AISTATS 2022 Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data IJCAI 2022 Scale-invariant Learning by Physics Inversion NeurIPS 2022 Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems NeurIPS 2022 Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data L4DC 2021 Combining Physics and Machine Learning for Network Flow Estimation ICLR 2021 Discovering Hidden Physics Behind Transport Dynamics CVPR 2021 Learning-based State Reconstruction for a Scalar Hyperbolic PDE under noisy Lagrangian Sensing L4DC 2021 MC-LSTM: Mass-Conserving LSTM ICML 2021 Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling NeurIPS 2021 Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning IJCAI 2021 Physics-informed Spline Learning for Nonlinear Dynamics Discovery IJCAI 2021 Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling NeurIPS 2021 Using Physics Knowledge for Learning Rigid-body Forward Dynamics with Gaussian Process Force Priors CoRL 2021 Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints NeurIPS 2020 Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control ICLR 2020 Weak Form Generalized Hamiltonian Learning NeurIPS 2020 Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations JMLR 2018 HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems CoRL 2018