160 citations · 834 across the 51 of their papers we have counts for
5 papers · 1 filter
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium control
Shriram Chennakesavalu, Grant M. Rotskoff
Experimental advances enabling high-resolution external control create new opportunities to produce materials with exotic properties. In this work, we investigate how a multi-agent…
Probing the Theoretical and Computational Limits of Dissipative Design
Shriram Chennakesavalu, Grant M. Rotskoff
Self-assembly, the process by which interacting components form well-defined and often intricate structures, is typically thought of as a spontaneous process arising from equilibri…
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
Normalizing flows can generate complex target distributions and thus show promise in many applications in Bayesian statistics as an alternative or complement to MCMC for sampling p…
Learning nonequilibrium control forces to characterize dynamical phase transitions
Jiawei Yan, Hugo Touchette, Grant M. Rotskoff
Sampling the collective, dynamical fluctuations that lead to nonequilibrium pattern formation requires probing rare regions of trajectory space. Recent approaches to this problem b…
Adaptive Monte Carlo augmented with normalizing flows
Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
Many problems in the physical sciences, machine learning, and statistical inference necessitate sampling from a high-dimensional, multi-modal probability distribution. Markov Chain…