3 papers
cs.LG2026
Global Convergence of DGM and PINN Algorithms for Solving Nonlinear PDEs
Justin Sirignano, Konstantinos Spiliopoulos, Samuel Cohen
The Deep Galerkin Method (DGM) and Physics Informed Neural Networks (PINNs) have become widely-used methods for solving partial differential equations (PDEs) in the rapidly growing…
cs.LG2026
Deep Hilbert--Galerkin Methods for Infinite-Dimensional PDEs and Optimal Control
Samuel N. Cohen, Filippo de Feo, Jackson Hebner +1
We develop deep learning-based approximation methods for fully nonlinear second-order PDEs on separable Hilbert spaces, such as HJB equations for infinite-dimensional control, by p…
econ.EM2025
Nowcasting using regression on signatures
Samuel N. Cohen, Giulia Mantoan, Lars Nesheim +3
We introduce a new method of nowcasting using regression on path signatures. Path signatures capture the geometric properties of sequential data. Because signatures embed observati…