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[1] Frédo Durand,et al. Taichi , 2019, ACM Trans. Graph..
[2] William H. Press,et al. Numerical Recipes 3rd Edition: The Art of Scientific Computing , 2007 .
[3] Bernhard Thomaszewski,et al. ADD , 2020, ACM Trans. Graph..
[4] Connor Schenck,et al. SPNets: Differentiable Fluid Dynamics for Deep Neural Networks , 2018, CoRL.
[5] Joshua B. Tenenbaum,et al. A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.
[6] Vaibhav Dixit,et al. A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions , 2018, 2021 IEEE High Performance Extreme Computing Conference (HPEC).
[7] E. Todorov,et al. A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems , 2005, Proceedings of the 2005, American Control Conference, 2005..
[8] David Baraff,et al. Fast contact force computation for nonpenetrating rigid bodies , 1994, SIGGRAPH.
[9] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[10] Daniel L. K. Yamins,et al. Flexible Neural Representation for Physics Prediction , 2018, NeurIPS.
[11] Vladlen Koltun,et al. Learning to Control PDEs with Differentiable Physics , 2020, ICLR.
[12] C.J.F. Ridders,et al. Accurate computation of F′(x) and F′(x) F″(x) , 1982 .
[13] Yvon Jarny,et al. A General Optimization Method using Adjoint Equation for Solving Multidimensional Inverse Heat Conduction , 1991 .
[14] Siddhartha S. Srinivasa,et al. DART: Dynamic Animation and Robotics Toolkit , 2018, J. Open Source Softw..
[15] Eric Heiden,et al. Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics , 2020, ICRA 2020.
[16] Abdeslam Boularias,et al. Learning to Slide Unknown Objects with Differentiable Physics Simulations , 2020, Robotics: Science and Systems.
[17] Yuval Tassa,et al. MuJoCo: A physics engine for model-based control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[18] Gaurav S. Sukhatme,et al. Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap , 2020, ArXiv.
[19] Ming C. Lin,et al. Differentiable Cloth Simulation for Inverse Problems , 2019, NeurIPS.
[20] Junggon Kim. Lie Group Formulation of Articulated Rigid Body Dynamics , 2012 .
[21] Marc Toussaint,et al. Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning , 2018, Robotics: Science and Systems.
[22] Yuval Tassa,et al. Synthesis and stabilization of complex behaviors through online trajectory optimization , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[23] Evangelos A. Theodorou,et al. Constrained Sampling-based Trajectory Optimization using Stochastic Approximation , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).
[24] Benjamin Schrauwen,et al. Automated Design of Complex Dynamic Systems , 2014, PloS one.
[25] Steven M. Seitz,et al. Interactive manipulation of rigid body simulations , 2000, SIGGRAPH.
[26] Joshua B. Tenenbaum,et al. End-to-End Differentiable Physics for Learning and Control , 2018, NeurIPS.
[27] A. Iollo,et al. An aerodynamic optimization method based on the inverse problem adjoint equations , 2001 .
[28] F. Jourdan,et al. A Gauss-Seidel like algorithm to solve frictional contact problems , 1998 .
[29] Jonas Degrave,et al. A DIFFERENTIABLE PHYSICS ENGINE FOR DEEP LEARNING IN ROBOTICS , 2016, Front. Neurorobot..
[30] Nicolas Mansard,et al. Analytical Derivatives of Rigid Body Dynamics Algorithms , 2018, Robotics: Science and Systems.
[31] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[32] J. Zico Kolter,et al. OptNet: Differentiable Optimization as a Layer in Neural Networks , 2017, ICML.
[33] Jiancheng Liu,et al. ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[34] Adrien Treuille,et al. Fluid control using the adjoint method , 2004, ACM Trans. Graph..
[35] Frédo Durand,et al. DiffTaichi: Differentiable Programming for Physical Simulation , 2020, ICLR.
[36] Ming C. Lin,et al. Scalable Differentiable Physics for Learning and Control , 2020, ICML.
[37] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[38] Gaurav S. Sukhatme,et al. Interactive Differentiable Simulation , 2019, ArXiv.
[39] David Q. Mayne,et al. Differential dynamic programming , 1972, The Mathematical Gazette.