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Awesome Intuitive PhysicsAwesome

A curated list of awesome Intuitive Physics resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, and awesome-architecture-search

Intuitive Physics is about utilizing machine learning techniques to learn physics. These are some of the awesome resources! This topic is also strongly related with Model-based Reinforcement Learning.

Contributing

Please help contribute this list by adding pull request

Markdown format:

- Paper Name [[pdf]](link) [[code]](link)
  - Author 1, Author 2 and Author 3. *Conference/Journal Year*

Table of Contents

Thesis

Survey

Conference Papers

  • Fast, Robust Adaptive Control by Learning only Forward Models. [pdf]
    • Andrew W. Moore. NIPS 1991
  • Locally Weighted Learning for Control. [pdf]
    • Christopher G. Atkeson, Andrew W. Moore, and Stefan Schaal. AI Review 1996
  • Interaction Networks for Learning about Objects, Relations and Physics. [pdf] [code]
    • Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu. NIPS 2016
  • A Compositional Object-Based Approach to Learning Physical Dynamics. [pdf] [code]
    • Michael B. Chang, Tomer Ullman, Antonio Torralba, Joshua B. Tenenbaum. ICLR 2017
  • Graph networks as learnable physics engines for inference and control . [pdf] [code]
    • Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia. ICML 2018
  • Flexible Neural Representation for Physics Prediction. [pdf] [code]
    • Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins.
  • Interpretable Intuitive Physics Model. [pdf] [code]
    • Tian Ye, Xiaolong Wang, James Davidson, Abhinav Gupta. ECCV 2018
  • Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids. [pdf] [code]
    • Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, Antonio Torralba. ICLR 2019
  • Reasoning About Physical Interactions with Object-Oriented Prediction and Planning. [pdf]
    • Michael Janner, Sergey Levine, William T. Freeman, Joshua B. Tenenbaum, Chelsea Finn, Jiajun Wu. ICLR 2019
  • Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning. [pdf]
    • Michael Lutter, Christian Ritter, Jan Peters. ICLR 2019
  • Learning Protein Structure with a Differentiable Simulator. [pdf]
    • John Ingraham, Adam Riesselman, Chris Sander, Debora Marks. ICLR 2019
  • Differentiable Physics-informed Graph Networks. [pdf]
    • Sungyong Seo, Yan Liu. ArXiv

Journal Papers

Tutorials

Tools

License

To the extent possible under law, we have waived all copyright and related or neighboring rights to this work.