Solving high-dimensional partial differential equations using deep learning von Han, Jiequn, Jentzen, Arnulf, E, Weinan

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Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations von Beck, Christian, E, Weinan, Jentzen, Arnulf

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Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations von E, Weinan, Han, Jiequn, Jentzen, Arnulf

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Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning von E, Weinan, Han, Jiequn, Jentzen, Arnulf

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A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions von Jentzen, Arnulf, Riekert, Adrian

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DNN Expression Rate Analysis of High-Dimensional PDEs: Application to Option Pricing von Elbrächter, Dennis, Grohs, Philipp, Jentzen, Arnulf, Schwab, Christoph

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On Multilevel Picard Numerical Approximations for High-Dimensional Nonlinear Parabolic Partial Differential Equations and High-Dimensional Nonlinear Backward Stochastic Differentia... von E, Weinan, Hutzenthaler, Martin, Jentzen, Arnulf, Kruse, Thomas

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Overcoming the Curse of Dimensionality in the Numerical Approximation of Parabolic Partial Differential Equations with Gradient-Dependent Nonlinearities von Hutzenthaler, Martin, Jentzen, Arnulf, Kruse, Thomas

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Weak Convergence Rates for Euler-Type Approximations of Semilinear Stochastic Evolution Equations with Nonlinear Diffusion Coefficients von Jentzen, Arnulf, Kurniawan, Ryan

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Weak convergence rates for temporal numerical approximations of the semilinear stochastic wave equation with multiplicative noise von Cox, Sonja, Jentzen, Arnulf, Lindner, Felix

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Solving high-dimensional optimal stopping problems using deep learning von BECKER, SEBASTIAN, CHERIDITO, PATRICK, JENTZEN, ARNULF, WELTI, TIMO

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Strong Overall Error Analysis for the Training of Artificial Neural Networks Via Random Initializations von Jentzen, Arnulf, Riekert, Adrian

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Landscape Analysis for Shallow Neural Networks: Complete Classification of Critical Points for Affine Target Functions von Cheridito, Patrick, Jentzen, Arnulf, Rossmannek, Florian

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Exponential integrability properties of numerical approximation processes for nonlinear stochastic differential equations von HUTZENTHALER, MARTIN, JENTZEN, ARNULF, WANG, XIAOJIE

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LOSS OF REGULARITY FOR KOLMOGOROV EQUATIONS von Hairer, Martin, Hutzenthaler, Martin, Jentzen, Arnulf

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Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations von Hutzenthaler, Martin, Jentzen, Arnulf, Kruse, Thomas, Nguyen, Tuan Anh, von Wurstemberger, Philippe

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Space-time error estimates for deep neural network approximations for differential equations von Grohs, Philipp, Hornung, Fabian, Jentzen, Arnulf, Zimmermann, Philipp

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Multilevel Picard iterations for solving smooth semilinear parabolic heat equations von E, Weinan, Hutzenthaler, Martin, Jentzen, Arnulf, Kruse, Thomas

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GALERKIN APPROXIMATIONS FOR THE STOCHASTIC BURGERS EQUATION von BLÖMKER, DIRK, JENTZEN, ARNULF

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Uniform error estimates for artificial neural network approximations for heat equations von Gonon, Lukas, Grohs, Philipp, Jentzen, Arnulf, Kofler, David, Šiška, David

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