Physics-Informed Graph Neural Networks Enhance Scalability of Variational Nonequilibrium Optimal Control


Sampling the collective, dynamical fluctuations that lead to nonequilibrium pattern formation requires probing rare regions of trajectory space. Recent approaches to this problem based on importance sampling, cloning, and spectral approximations, have yielded significant insight into nonequilibrium systems, but tend to scale poorly with the size of the system, especially near dynamical phase transitions. Here we propose a machine learning algorithm that samples rare trajectories and estimates the associated large deviation functions using a many-body control force by leveraging the flexible function representation provided by deep neural networks, importance sampling in trajectory space, and stochastic optimal control theory. We show that this approach scales to hundreds of interacting particles and remains robust at dynamical phase transitions.

J. Chem. Phys
Postdoctoral Researcher

Jiawei Yan is a Postdoctoral Researcher at Stanford University. His current research interests include biophysical self-assembly processes, complex networks, and fluctuations in living systems. Prior to Stanford University, he completed his Ph.D. at Harvard University in the MCO doctoral program, Engineering & Physical Biology track, advised by Dr. Johan Paulsson. His thesis discovered a novel class of trade-offs on the fluctuations of connected components in generic stochastic complex networks. Jiawei received a B.S. in the School of Life Sciences from Peking University.