Probability · Mathematics
Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations
Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse, Tuan Anh Nguyen +1
2021-03-09
Numerical Analysis · Mathematics
Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities
Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse, Tuan Anh Nguyen
2020-10-12
Probability · Mathematics
Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations
Christian Beck, Lukas Gonon, Arnulf Jentzen
2020-03-03
Numerical Analysis · Mathematics
Overcoming the curse of dimensionality in the numerical approximation of Allen-Cahn partial differential equations via truncated full-history recursive multilevel Picard approximations
Christian Beck, Fabian Hornung, Martin Hutzenthaler, Arnulf Jentzen +1
2021-03-09
Numerical Analysis · Mathematics
Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions
Victor Boussange, Sebastian Becker, Arnulf Jentzen, Benno Kuckuck +1
2022-05-10
Numerical Analysis · Mathematics
Multilevel Picard approximations overcome the curse of dimensionality in the numerical approximation of general semilinear PDEs with gradient-dependent nonlinearities
Ariel Neufeld, Tuan Anh Nguyen, Sizhou Wu
2025-03-21
Numerical Analysis · Mathematics
Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities
Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse
2021-10-12
Numerical Analysis · Mathematics
On multilevel Picard numerical approximations for high-dimensional nonlinear parabolic partial differential equations and high-dimensional nonlinear backward stochastic differential equations
Weinan E, Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse
2020-11-25
Numerical Analysis · Mathematics
A learning scheme by sparse grids and Picard approximations for semilinear parabolic PDEs
Jean-François Chassagneux, Junchao Chen, Noufel Frikha, Chao Zhou
2021-02-25
Numerical Analysis · Mathematics
Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations
Sebastian Becker, Ramon Braunwarth, Martin Hutzenthaler, Arnulf Jentzen +1
2021-10-12
Numerical Analysis · Mathematics
A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations
Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse, Tuan Anh Nguyen
2020-11-25
Numerical Analysis · Mathematics
Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense
Ariel Neufeld, Tuan Anh Nguyen
2026-03-24
Numerical Analysis · Mathematics
Strong $L^p$-error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations
Martin Hutzenthaler, Arnulf Jentzen, Benno Kuckuck, Joshua Lee Padgett
2021-10-26
Numerical Analysis · Mathematics
Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks
Martin Hutzenthaler, Arnulf Jentzen, Philippe von Wurstemberger
2020-10-05
Numerical Analysis · Mathematics
An overview on deep learning-based approximation methods for partial differential equations
Christian Beck, Martin Hutzenthaler, Arnulf Jentzen, Benno Kuckuck
2023-02-10