Accelerating Particle and Fluid Simulations with Differentiable Graph Networks for Solving Forward and Inverse Problems

Author:

Kumar Krishna1ORCID,Choi Yonjin2ORCID

Affiliation:

1. University of Texas System, United States of America

2. Univeristy of Texas at Austin, United States of America

Funder

National Science Foundation

Publisher

ACM

Reference20 articles.

1. Inverse Design for Fluid-Structure Interactions using Graph Network Simulators;Allen Kelsey;Advances in Neural Information Processing Systems,2022

2. José  E Andrade and Utkarsh Mital . 2019. Multiscale and Multiphysics Modeling of Soils . In Geotechnical Fundamentals for Addressing New World Challenges . Springer , 141–168. José E Andrade and Utkarsh Mital. 2019. Multiscale and Multiphysics Modeling of Soils. In Geotechnical Fundamentals for Addressing New World Challenges. Springer, 141–168.

3. Peter  W Battaglia , Jessica  B Hamrick , Victor Bapst , Alvaro Sanchez-Gonzalez , Vinicius Zambaldi , Mateusz Malinowski , Andrea Tacchetti , David Raposo , Adam Santoro , Ryan Faulkner , 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 ( 2018 ). Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018).

4. Yongjin Choi and Krishna Kumar . 2023. Graph Neural Network-based surrogate model for granular flows. arXiv preprint arXiv:2305.05218 ( 2023 ). Yongjin Choi and Krishna Kumar. 2023. Graph Neural Network-based surrogate model for granular flows. arXiv preprint arXiv:2305.05218 (2023).

5. Do E. 2019 . Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. https://www.osti.gov/biblio/1478744 DoE. 2019. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. https://www.osti.gov/biblio/1478744

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