6 papers
Finite-Sample Metric Non-Collapse for Geometrically Supervised Latent World Models in Control
Alain Bensoussan, Minh-Nhat Phung, Minh-Binh Tran
We establish a finite-sample learning-to-control theory for geometrically supervised latent models of nonlinear deterministic systems. Geometric supervision is used only during tra…
Computational Control of Nonlinear Partial Differential Equations Using Machine Learning
Maximilian Kurbanov, Minh-Nhat Phung, Minh-Binh Tran
The numerical reconstruction of controls for nonlinear partial differential equations (PDEs) remains a challenging and relatively underdeveloped problem, despite the extensive lite…
Control, Optimal Transport and Neural Differential Equations in Supervised Learning
Minh-Nhat Phung, Minh-Binh Tran
We study the fundamental computational problem of approximating optimal transport (OT) equations using neural differential equations (Neural ODEs). More specifically, we develop a…
A Relative Ignorability Framework for Decision-Relevant Observability in Control Theory and Reinforcement Learning
MaryLena Bleile, Minh-Nhat Phung, Minh-Binh Tran
Sequential decision-making systems routinely operate with missing or incomplete data. Classical reinforcement learning theory, which is commonly used to solve sequential decision p…
Controlling the Rates of a Chain of Harmonic Oscillators with a Point Langevin Thermostat
Amirali Hannani, Minh-Binh Tran, Minh Nhat Phung +1
We consider the control problem of controlling the rates of an infinite chain of coupled harmonic oscillators with a Langevin thermostat at the origin. We study the effect of two t…
Internal Control of The Transition Kernel for Stochastic Lattice Dynamics
Amirali Hannani, Minh-Nhat Phung, Minh-Binh Tran +1
In [5], we have designed impulsive and feedback controls for harmonic chains with a point thermostat. In this work, we study the internal control for stochastic lattice dynamics, w…