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Learning Efficient Hybrid Particle-continuum Representations of Non-equilibrium N-body Systems
Tailin Wu · Michael Sun · Hsuan-Gu Chou · Pranay Reddy Samala · Sithipont Cholsaipant · Sophia Kivelson · Jacqueline Yau · Rex Ying · E. Paulo Alves · Jure Leskovec · Frederico Fiuza
Event URL: https://openreview.net/forum?id=Rd68eTARk4 »

An important class of multi-scale, non-equilibrium, N-body physical systems deals with an interplay between particle and continuum phenomena. These include hypersonic flow and plasma dynamics, materials science, and astrophysics. Hybrid solvers that combine particle and continuum representations could provide an efficient framework to model these systems. However, the coupling between these two representations has been a key challenge, which is often limited to inaccurate or incomplete prescriptions. In this work, we introduce a method for Learning Hybrid Particle-Continuum (LHPC) models from the data of first-principles particle simulations. LHPC analyzes the local velocity-space particle distribution function and separates it into near-equilibrium (thermal) and far-from-equilibrium (non-thermal) components. The most computationally-intensive particle solver is used to advance the non-thermal particles, whereas a neural network solver is used to efficiently advance the thermal component using a continuum representation. Most importantly, an additional neural network learns the particle-continuum coupling: the dynamical exchange of mass, momentum, and energy between the particle and continuum representations. Training of the different neural network components is done in an integrated manner to ensure global consistency and stability of the LHPC model. We demonstrate our method in an intense laser-plasma interaction problem involving highly nonlinear, far-from-equilibrium dynamics associated with the coupling between electromagnetic fields and multiple particle species. More efficient modeling of these interactions is critical for the design and optimization of compact accelerators for material science and medical applications. Our method achieves an important balance between accuracy and speed: LHPC is 8 times faster than a classical particle solver and achieves up to 6.8-fold reduction of long-term prediction error for key quantities of interest compared to deep-learning baselines using uniform representations.

Author Information

Tailin Wu (Stanford)
Tailin Wu

Tailin Wu is a postdoc researcher in the Department of Computer Science at Stanford University, working with professor Jure Leskovec. His research interests lies in AI for large-scale simulations of complex systems, AI for scientific discovery, and representation learning

Michael Sun (Computer Science Department, Stanford University)
Hsuan-Gu Chou (Stanford University)
Pranay Reddy Samala (Stanford University)
Sithipont Cholsaipant (Computer Science Department, Stanford University)
Sophia Kivelson (Stanford University)
Jacqueline Yau (Stanford University)

Jacqueline Yau was a Master's student in Computer Science at Stanford University, obtaining her degree recently. She is interested in computer vision, privacy and fairness, graph representation, and general machine learning. She has worked on symmetry detection in objects, physics simulation, and the intersection of vision and audio. Currently she is working at Apple as a Machine Learning Engineer. She also received her Bachelor's degree with departmental honors in Computer Science at Stanford University.

Rex Ying (Yale University)
E. Paulo Alves (University of California, Los Angeles)
Jure Leskovec (Stanford University/Pinterest)
Frederico Fiuza (Stanford University)

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