Deep Learning for Physics Simulation

Author:

Du Tao1ORCID

Affiliation:

1. Institute for Interdisciplinary Information Sciences, Tsinghua University, China

Publisher

ACM

Reference47 articles.

1. Anurag Ajay , Jiajun Wu , Nima Fazeli , Maria Bauza , Leslie P Kaelbling , Joshua B Tenenbaum , and Alberto Rodriguez . 2018 . Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Anurag Ajay, Jiajun Wu, Nima Fazeli, Maria Bauza, Leslie P Kaelbling, Joshua B Tenenbaum, and Alberto Rodriguez. 2018. Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

2. Joe Allen . 1972 . The Apollo 15 Hammer-Feather Drop . Retrieved February 18, 2023 from https://moon.nasa.gov/resources/331/the-apollo-15-hammer-feather-drop/ Joe Allen. 1972. The Apollo 15 Hammer-Feather Drop. Retrieved February 18, 2023 from https://moon.nasa.gov/resources/331/the-apollo-15-hammer-feather-drop/

3. A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling

4. Uri M. Ascher and Linda R . Petzold . 1998 . Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations (1st ed.). Society for Industrial and Applied Mathematics, USA. Uri M. Ascher and Linda R. Petzold. 1998. Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations (1st ed.). Society for Industrial and Applied Mathematics, USA.

5. Learning data-driven discretizations for partial differential equations

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