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Found 4 papers, 1 papers with code
FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
A key shortcoming of these supervised learning methods is their need for large training data sets, typically generated from particle models in conjunction with complex numerical forward models simulating the physics of transmission electron microscopes.
Neural and spectral operator surrogates: unified construction and expression rate bounds
Specifically, we study approximation rates for Deep Neural Operator and Generalized Polynomial Chaos (gpc) Operator surrogates for nonlinear, holomorphic maps between infinite-dimensional, separable Hilbert spaces.
Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations
The approximation of solutions to second order Hamilton--Jacobi--Bellman (HJB) equations by deep neural networks is investigated.
Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions
In recent work it has been established that deep neural networks are capable of approximating solutions to a large class of parabolic partial differential equations without incurring the curse of dimension.
