4 papers · 1 filter
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…
Neural-Spectral and Newton-HUM Methods for Reconstructing Controls of Wave Equations
Tan-Phuc Nguyen, Minh-Binh Tran, Son Tu
We develop a framework for constructing and rigorously validating controls for semilinear wave equations with localized interior control. We analyze the continuous energy-space pro…
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…
Operator Splitting, Policy Iteration, and Machine Learning for Stochastic Optimal Control
Alain Bensoussan, Thien P. B. Nguyen, Minh-Binh Tran +1
We propose a splitting approach to solve the second-order Hamilton--Jacobi equation, reducing it to a heat step and a purely first-order step. The latter is implemented using a gra…