Learning Physics-Consistent Particle Interactions
- URL: http://arxiv.org/abs/2202.00299v1
- Date: Tue, 1 Feb 2022 09:59:53 GMT
- Title: Learning Physics-Consistent Particle Interactions
- Authors: Zhichao Han, David S. Kammer, Olga Fink
- Abstract summary: We propose an algorithm that adapts the Graph Networks framework, which contains an edge part to learn the pairwise interaction and a node part to model the dynamics at particle level.
We test the proposed methodology on multiple datasets and demonstrate that it achieves considerably better performance in inferring correctly the pairwise interactions.
- Score: 3.9686511558236055
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Interacting particle systems play a key role in science and engineering.
Access to the governing particle interaction law is fundamental for a complete
understanding of such systems. However, the inherent system complexity keeps
the particle interaction hidden in many cases. Machine learning methods have
the potential to learn the behavior of interacting particle systems by
combining experiments with data analysis methods. However, most existing
algorithms focus on learning the kinetics at the particle level. Learning
pairwise interaction, e.g., pairwise force or pairwise potential energy,
remains an open challenge. Here, we propose an algorithm that adapts the Graph
Networks framework, which contains an edge part to learn the pairwise
interaction and a node part to model the dynamics at particle level. Different
from existing approaches that use neural networks in both parts, we design a
deterministic operator in the node part. The designed physics operator on the
nodes restricts the output space of the edge neural network to be exactly the
pairwise interaction. We test the proposed methodology on multiple datasets and
demonstrate that it achieves considerably better performance in inferring
correctly the pairwise interactions while also being consistent with the
underlying physics on all the datasets than existing purely data-driven models.
The developed methodology can support a better understanding and discovery of
the underlying particle interaction laws, and hence guide the design of
materials with targeted properties.
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