% Key papers for Physics-informed dynamics learning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2019_hamiltonian_58362,
  title = {{Hamiltonian Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14434},
}

@inproceedings{neurips2018_end_57095,
  title = {{End-to-End Differentiable Physics for Learning and Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11691},
}

@inproceedings{iclr2019_learning_29158,
  title = {{Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/852},
}

@inproceedings{neurips2018_flexible_57140,
  title = {{Flexible neural representation for physics prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11840},
}

@inproceedings{iclr2017_a_28230,
  title = {{A Compositional Object-Based Approach to Learning Physical Dynamics}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=Bkab5dqxe},
}

@inproceedings{iclr2020_symplectic_29916,
  title = {{Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=ryxmb1rKDS},
}

@inproceedings{iclr2019_deep_28808,
  title = {{Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/916},
}

@inproceedings{iclr2021_incorporating_30744,
  title = {{Incorporating Symmetry into Deep Dynamics Models for Improved Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2785},
}

@inproceedings{iclr2020_lagrangian_29258,
  title = {{Lagrangian Fluid Simulation with Continuous Convolutions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=B1lDoJSYDH},
}

@inproceedings{neurips2019_differentiable_58126,
  title = {{Differentiable Cloth Simulation for Inverse Problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13308},
}

@inproceedings{iclr2020_hamiltonian_29448,
  title = {{Hamiltonian Generative Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HJenn6VFvB},
}

@inproceedings{iclr2021_learning_30671,
  title = {{Learning Mesh-Based Simulation with Graph Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2837},
}

@inproceedings{iclr2020_physics_29753,
  title = {{Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=r1gelyrtwH},
}

@inproceedings{icml2018_learning_36818,
  title = {{Learning unknown ODE models with Gaussian processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2277},
}

@inproceedings{icml2020_scalable_38730,
  title = {{Scalable Differentiable Physics for Learning and Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5767},
}

@inproceedings{cvpr2021_physics_17603,
  title = {{Physics-Based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2024_universal_73890,
  title = {{Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93621},
}

@inproceedings{icml2021_data_39197,
  title = {{Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9377},
}

@inproceedings{neurips2020_simplifying_60832,
  title = {{Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18346},
}

@inproceedings{aaai2021_differentiable_425,
  title = {{Differentiable Fluids with Solid Coupling for Learning and Control}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06138-differentiable-fluids-with-solid-coupling-for-learning-and-control/},
}

@inproceedings{neurips2024_the_70114,
  title = {{The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97882},
}

@inproceedings{neurips2024_input_73606,
  title = {{Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93881},
}

@inproceedings{neurips2024_windsorml_72538,
  title = {{WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97656},
}

@inproceedings{cvpr2024_bayesian_22822,
  title = {{Bayesian Differentiable Physics for Cloth Digitalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29583},
}

@inproceedings{neurips2025_stable_78455,
  title = {{Stable Port-Hamiltonian Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=epIGnGgcKD},
}

@inproceedings{aaai2024_neural_6770,
  title = {{Neural Time-Reversed Generalized Riccati Equation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28630},
}

@inproceedings{icml2024_equivariant_44908,
  title = {{Equivariant Graph Neural Operator for Modeling 3D Dynamics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33544},
}

@inproceedings{neurips2024_neuralfluid_71682,
  title = {{NeuralFluid: Nueral Fluidic System Design and Control with Differentiable Simulation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95600},
}

@inproceedings{iclr2024_compositional_35593,
  title = {{Compositional Generative Inverse Design}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17480},
}

@inproceedings{icml2024_harnessing_44488,
  title = {{Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33964},
}

@inproceedings{aaai2024_learning_6478,
  title = {{Learning Reduced Fluid Dynamics}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29367},
}

@inproceedings{cvpr2025_uniphy_28002,
  title = {{UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34241},
}

@inproceedings{iclr2024_learning_33766,
  title = {{Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19309},
}

@inproceedings{neurips2024_diffphycon_71788,
  title = {{DiffPhyCon: A Generative Approach to Control Complex Physical Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95505},
}

@inproceedings{neurips2024_deeplag_74158,
  title = {{DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93384},
}

@inproceedings{iclr2024_space_33631,
  title = {{Space and time continuous physics simulation from partial observations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19444},
}

@inproceedings{iclr2024_latent_34602,
  title = {{Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18472},
}

@inproceedings{iclr2024_interpretable_35230,
  title = {{Interpretable Meta-Learning of Physical Systems}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17843},
}

@inproceedings{icml2024_discovering_43464,
  title = {{Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34988},
}