Strong overall error analysis for the training of artificial neural networks via random initializations

Jentzen A, Riekert A (2023)

In: Communications in Mathematics and Statistics, 0.

Forschungsartikel (Zeitschrift)

Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation

Jentzen A, Riekert A (2023)

In: Journal of Mathematical Analysis and Applications, 517(2), 126601.

Forschungsartikel (Zeitschrift)

Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality

Grohs P, Ibragimov S, Jentzen A, Koppensteiner S (2023)

In: Journal of Complexity, 0, 101746.

Forschungsartikel (Zeitschrift)

Strong convergence rates for explicit space-time discrete numerical approximations of stochastic Allen--Cahn equations

Becker S, Gess B, Jentzen A, Kloeden PE (2023)

In: Stochastics and Partial Differential Equations: Analysis and Computations, 11(1).

Forschungsartikel (Zeitschrift)

The necessity of depth for artificial neural networks to approximate certain classes of smooth bounded functions without the curse of dimensionality

Gonon, L.; Graeber, R.; Jentzen, A. (2023)

In: arXiv.org:2212.13111.

Forschungsartikel in Online-Sammlung

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Strong overall error analysis for the training of artificial neural networks via random initializations Jentzen A, Riekert A (2023) In: Communications in Mathematics and Statistics, 0. Forschungsartikel (Zeitschrift)
An overview on deep learning-based approximation methods for partial differential equations Beck C, Hutzenthaler M, Jentzen A, Kuckuck B (2023) In: Discrete and Continuous Dynamical Systems - Series B, 28(6). Forschungsartikel (Zeitschrift)
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation Jentzen A, Riekert A (2023) In: Journal of Mathematical Analysis and Applications, 517(2), 126601. Forschungsartikel (Zeitschrift)
Overcoming the curse of dimensionality in the numerical approximation of backward stochastic differential equations Hutzenthaler M, Jentzen A, Kruse T, Nguyen TA (2023) In: Journal of Numerical Mathematics, 31(2). Forschungsartikel (Zeitschrift)
Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality Grohs P, Ibragimov S, Jentzen A, Koppensteiner S (2023) In: Journal of Complexity, 0, 101746. Forschungsartikel (Zeitschrift)
Space-time error estimates for deep neural network approximations for differential equations Grohs P, Hornung F, Jentzen A, Zimmermann P (2023) In: Advances in Computational Mathematics, 49(1), 4. Forschungsartikel (Zeitschrift)
Strong convergence rates for explicit space-time discrete numerical approximations of stochastic Allen--Cahn equations Becker S, Gess B, Jentzen A, Kloeden PE (2023) In: Stochastics and Partial Differential Equations: Analysis and Computations, 11(1). Forschungsartikel (Zeitschrift)
Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions Boussange, V.; Becker, S.; Jentzen, A.; Kuckuck, B.; Pellissier, L. (2023) In: SN Partial Differential Equations and Applications, 4, 51. Forschungsartikel (Zeitschrift)
The necessity of depth for artificial neural networks to approximate certain classes of smooth bounded functions without the curse of dimensionality Gonon, L.; Graeber, R.; Jentzen, A. (2023) In: arXiv.org:2212.13111. Forschungsartikel in Online-Sammlung
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations Jentzen, A.; Riekert, A.; von Wurstemberger, P. (2023) In: arXiv.org:2302.03286. Forschungsartikel in Online-Sammlung