Fetching the paper…
Reading the bibliography…
Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments.
Physically based modeling: Principles and practice
Witkin, A. and Baraff, D · 1997
Earlier work this paper cites.
Robust treatment of collisions, contact and friction for cloth animation
Bridson, R., Fedkiw, R., and Anderson, J · 2002
Earlier work this paper cites.
Robust treatment of simultaneous collisions
Harmon, D., Vouga, E., Tamstorf, R., and Grinspun, E · 2008
Earlier work this paper cites.
Polygon mesh processing
Botsch, M., Kobbelt, L., Pauly, M., Alliez, P., and Lévy, B · 2010
Earlier work this paper cites.
Adaptive anisotropic remeshing for cloth simulation
Narain, R., Samii, A., and O’Brien, J. F · 2012
Earlier work this paper cites.
MuJoCo: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
Earlier work this paper cites.
Unified particle physics for real-time applications
Macklin, M., Müller, M., Chentanez, N., and Kim, T · 2014
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Battaglia, P. W., Pascanu, R., Lai, M., Rezende, D., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
The CMA evolution strategy: A tutorial
Hansen, N · 2016
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2016
Earlier work this paper cites.
OptNet: Differentiable optimization as a layer in neural networks
Amos, B. and Kolter, J. Z · 2017
Cited alongside, same era.
A compositional object-based approach to learning physical dynamics
Chang, M. B., Ullman, T., Torralba, A., and Tenenbaum, J. B · 2017
Cited alongside, same era.
Analytical derivatives of rigid body dynamics algorithms
Carpentier, J. and Mansard, N · 2018
Cited alongside, same era.
End-to-end differentiable physics for learning and control
de Avila Belbute-Peres, F., Smith, K. A., Allen, K. R., Tenenbaum, J., and Kolter, J. Z · 2018
Cited alongside, same era.
Flexible neural representation for physics prediction
Mrowca, D., Zhuang, C., Wang, E., Haber, N., Li, F., Tenenbaum, J., and Yamins, D. L · 2018
Cited alongside, same era.
Graph networks as learnable physics engines for inference and control
Sanchez-Gonzalez, A., Heess, N., Springenberg, J. T., Merel, J., Riedmiller, M., Hadsell, R., and Battaglia, P · 2018
ChainQueen: A real-time differentiable physical simulator for soft robotics
Hu, Y., Liu, J., Spielberg, A., Tenenbaum, J. B., Freeman, W. T., Wu, J., Rus, D., and Matusik, W · 2019
Later among the works it cites.
Learning protein structure with a differentiable simulator
Ingraham, J., Riesselman, A., Sander, C., and Marks, D · 2019
Later among the works it cites.
Differentiable cloth simulation for inverse problems
Liang, J., Lin, M. C., and Koltun, V · 2019
Later among the works it cites.
PyTorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Later among the works it cites.
Learning-in-the-loop optimization: End-to-end control and co-design of soft robots through learned deep latent representations
Spielberg, A., Zhao, A., Hu, Y., Du, T., Matusik, W., and Rus, D · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
SPNets: Differentiable fluid dynamics for deep neural networks
Schenck, C. and Fox, D · 2018
Cited alongside, same era.
Differentiable physics and stable modes for tool-use and manipulation planning
Toussaint, M., Allen, K., Smith, K., and Tenenbaum, J · 2018
Cited alongside, same era.
A differentiable physics engine for deep learning in robotics
Degrave, J., Hermans, M., Dambre, J., and wyffels, F · 2019
Cited alongside, same era.
Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Li, Y., Wu, J., Tedrake, R., Tenenbaum, J. B., and Torralba, A
Cited in the paper.
Propagation networks for model-based control under partial observation
Li, Y., Wu, J., Zhu, J.-Y., Tenenbaum, J. B., Torralba, A., and Tedrake, R
Cited in the paper.
Learning to control PDEs with differentiable physics
Holl, P., Koltun, V., and Thuerey, N · 2020
Closest in time.
DiffTaichi: Differentiable programming for physical simulation
Hu, Y., Anderson, L., Li, T., Sun, Q., Carr, N., Ragan-Kelley, J., and Durand, F · 2020
Closest in time.
Automatic differentiation and continuous sensitivity analysis of rigid body dynamics
Millard, D., Heiden, E., Agrawal, S., and Sukhatme, G. S · 2020
Closest in time.
Lagrangian fluid simulation with continuous convolutions
Ummenhofer, B., Prantl, L., Thürey, N., and Koltun, V · 2020
Closest in time.