STMD distills the full transition map of diffusion sampling SDEs into a conditional Mean Flow model to enable fast one- or few-step stochastic sampling without teacher models or bi-level optimization.
An overview on deep learning-based approximation methods for partial differen tial equations
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DNNs approximate sequences of functions constructed via finite compositions of locally Lipschitz continuous functions, maxima, and products with polynomial parameter growth in d and 1/ε.
Random neural networks achieve a dimension-free approximation rate of 1/2 for sufficiently regular time-dependent Sobolev functions and can efficiently approximate solutions to Porous Medium Equations and Compressible Navier-Stokes Equations.
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Deep neural network approximation theory for high-dimensional functions
DNNs approximate sequences of functions constructed via finite compositions of locally Lipschitz continuous functions, maxima, and products with polynomial parameter growth in d and 1/ε.