FNO extended to complex frequencies via Ehrenpreis-Palamodov principle improves state and optimal control learning for PDE systems, with order-of-magnitude lower training errors and better non-periodic boundary predictions on nonlinear Burgers' equation.
A comprehensive study of adjoint-based optimization of non-linear systems with appli- cation to burgers’ equation,
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FNO$^{\angle \theta}$: Extended Fourier neural operator for learning state and optimal control of distributed parameter systems
FNO extended to complex frequencies via Ehrenpreis-Palamodov principle improves state and optimal control learning for PDE systems, with order-of-magnitude lower training errors and better non-periodic boundary predictions on nonlinear Burgers' equation.