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math.OC2026
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…
math.OC2026
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…
math.OC2026
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…