Uniform convergence guarantees for the deep Ritz method for nonlinear problems
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Publication:2110466
DOI10.1186/s13662-022-03722-8OpenAlexW3212352657MaRDI QIDQ2110466
Publication date: 21 December 2022
Published in: Advances in Continuous and Discrete Models (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2111.05637
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- Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
- On the limited memory BFGS method for large scale optimization
- Applied functional analysis. Functional analysis, Sobolev spaces and elliptic differential equations
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- DGM: a deep learning algorithm for solving partial differential equations
- Nonlinear functional analysis. An introduction
- Neural algorithm for solving differential equations
- An overview on deep learning-based approximation methods for partial differential equations
- Neural‐network‐based approximations for solving partial differential equations
- Norm-resolvent convergence in perforated domains
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning
- Convergence Rate Analysis for Deep Ritz Method
- Finite Neuron Method and Convergence Analysis
- Free Energy of a Nonuniform System. I. Interfacial Free Energy
- Finite Elements
- Linear functional analysis. An application oriented introduction
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