Paper extends Fourier Neural Operators to solve optimal control problems in distributed parameter systems (e.g., PDEs). The extension enables FNOs to learn both system state evolution and optimal control policies simultaneously, combining state-learning with control optimization.
Research
FNO$^{\angle \theta}$: Extended Fourier neural operator for learning state and optimal control of distributed parameter systems
Fourier Neural Operators extended to jointly learn system dynamics and optimal control policies for PDEs, enabling end-to-end policy optimization in distributed parameter systems.
Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
Tags
research