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Benno Kuckuck

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning

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Aug 23, 2024
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Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

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Oct 31, 2023
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Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense

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Sep 24, 2023
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Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions

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May 07, 2022
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An overview on deep learning-based approximation methods for partial differential equations

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Dec 22, 2020
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Full error analysis for the training of deep neural networks

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Sep 30, 2019
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