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Functional SDE approximation inspired by a deep operator network architecture
A novel approach to approximate solutions of Stochastic Differential Equations (SDEs) by Deep Neural Networks is derived and analysed.
Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures
We sample from a given target distribution by constructing a neural network which maps samples from a simple reference, e. g. the standard normal distribution, to samples from the target.
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