Physics Informed Machine Learning
60 minutes
Fourier Neural Operators for Learning Solution Operators of PDEs
A neural-operator architecture that learns mappings between infinite-dimensional function spaces by parameterizing the integral kernel in Fourier space, applying a global convolution via the fast Fourier transform in each layer. Covers how this yields resolution-invariant, mesh-free surrogates for partial-differential-equation and wave-propagation problems.