2 papers
math.NA2025
Solving nonconvex Hamilton--Jacobi--Isaacs equations with PINN-based policy iteration
Hee Jun Yang, Minjung Gim, Yeoneung Kim
We propose a mesh-free policy iteration framework that combines classical dynamic programming with physics-informed neural networks (PINNs) to solve high-dimensional, nonconvex Ham…
math.NA2025
Numerical study on hyper parameter settings for neural network approximation to partial differential equations
Hee Jun Yang, Alexander Heinlein, Hyea Hyun Kim
Approximate solutions of partial differential equations (PDEs) obtained by neural networks are highly affected by hyper parameter settings. For instance, the model training strongl…