energy conservation

Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes L4DC 2026 Efficiently Parameterized Neural Metriplectic Systems ICLR 2025 Energy-consistent Neural Operators for Hamiltonian and Dissipative Partial Differential Equations AISTATS 2025 Generating Physical Dynamics under Priors ICLR 2025 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction ICML 2025 On Neural BRDFs: A Thorough Comparison of State-of-the-Art Approaches WACV 2025 Noether's Razor: Learning Conserved Quantities NeurIPS 2024 Structure-Preserving Physics-Informed Neural Networks with Energy or Lyapunov Structure IJCAI 2024 Symplectic Neural Gaussian Processes for Meta-learning Hamiltonian Dynamics IJCAI 2024 Enhancing the Inductive Biases of Graph Neural ODE for Modeling Physical Systems ICLR 2023 Microcanonical Hamiltonian Monte Carlo JMLR 2023 Reversible and irreversible bracket-based dynamics for deep graph neural networks NeurIPS 2023 Deconstructing the Inductive Biases of Hamiltonian Neural Networks ICLR 2022 Stiffness-aware neural network for learning Hamiltonian systems ICLR 2022 Structure-Preserving Learning Using Gaussian Processes and Variational Integrators L4DC 2022 Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo ICML 2021 Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems L4DC 2021 Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control RSS 2021 Scalable Graph Networks for Particle Simulations AAAI 2021 Sparse Symplectically Integrated Neural Networks NeurIPS 2020 Hamiltonian Neural Networks NeurIPS 2019