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Arnulf Jentzen

Showing results (1-10 of 7) with videos related to

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Proceedings. Mathematical, Physical, and Engineering Sciences|December 12, 2017
On stochastic differential equations with arbitrarily slow convergence rates for strong approximation in two space dimensionsMáté Gerencsér, Arnulf Jentzen, Diyora Salimova
Proceedings of the National Academy of Sciences of the United States of America|August 8, 2018
Solving high-dimensional partial differential equations using deep learningJiequn Han, Arnulf Jentzen, Weinan E
IEEE Transactions on Neural Networks and Learning Systems|January 29, 2021
Efficient Approximation of High-Dimensional Functions With Neural NetworksPatrick Cheridito, Arnulf Jentzen, Florian Rossmannek
SN Partial Differential Equations and Applications|October 14, 2024
Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equationsChristian Beck, Lukas Gonon, Arnulf Jentzen
Journal of Optimization Theory and Applications|December 12, 2024
Gradient Descent Provably Escapes Saddle Points in the Training of Shallow ReLU NetworksPatrick Cheridito, Arnulf Jentzen, Florian Rossmannek
Applied Mathematics and Optimization|February 7, 2025
Nonlinear Monte Carlo Methods with Polynomial Runtime for Bellman Equations of Discrete Time High-Dimensional Stochastic Optimal Control ProblemsChristian Beck, Arnulf Jentzen, Konrad Kleinberg, et al.
Proceedings. Mathematical, Physical, and Engineering Sciences|January 7, 2021
Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equationsMartin Hutzenthaler, Arnulf Jentzen, Thomas Kruse, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Proceedings. Mathematical, Physical, and Engineering Sciences|December 12, 2017
On stochastic differential equations with arbitrarily slow convergence rates for strong approximation in two space dimensionsMáté Gerencsér, Arnulf Jentzen, Diyora Salimova
Proceedings of the National Academy of Sciences of the United States of America|August 8, 2018
Solving high-dimensional partial differential equations using deep learningJiequn Han, Arnulf Jentzen, Weinan E
IEEE Transactions on Neural Networks and Learning Systems|January 29, 2021
Efficient Approximation of High-Dimensional Functions With Neural NetworksPatrick Cheridito, Arnulf Jentzen, Florian Rossmannek
SN Partial Differential Equations and Applications|October 14, 2024
Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equationsChristian Beck, Lukas Gonon, Arnulf Jentzen
Journal of Optimization Theory and Applications|December 12, 2024
Gradient Descent Provably Escapes Saddle Points in the Training of Shallow ReLU NetworksPatrick Cheridito, Arnulf Jentzen, Florian Rossmannek
Applied Mathematics and Optimization|February 7, 2025
Nonlinear Monte Carlo Methods with Polynomial Runtime for Bellman Equations of Discrete Time High-Dimensional Stochastic Optimal Control ProblemsChristian Beck, Arnulf Jentzen, Konrad Kleinberg, et al.
Proceedings. Mathematical, Physical, and Engineering Sciences|January 7, 2021
Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equationsMartin Hutzenthaler, Arnulf Jentzen, Thomas Kruse, et al.
Pageof 1