Philippe von Wurstemberger

According to our database, Philippe von Wurstemberger authored at least 7 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2023

Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory.

Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger

CoRR, 2023

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations.

Arnulf Jentzen, Adrian Riekert, Philippe von Wurstemberger

CoRR, 2023

2022

Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing.

Sebastian Becker, Arnulf Jentzen, Marvin S. Müller, Philippe von Wurstemberger

CoRR, 2022

2020

Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates.

Arnulf Jentzen, Philippe von Wurstemberger

J. Complex., 2020

High-dimensional approximation spaces of artificial neural networks and applications to partial differential equations.

Pierfrancesco Beneventano, Patrick Cheridito, Arnulf Jentzen, Philippe von Wurstemberger

CoRR, 2020

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, Philippe von Wurstemberger

CoRR, 2020

2018

A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations.

Philipp Grohs, Fabian Hornung, Arnulf Jentzen, Philippe von Wurstemberger

CoRR, 2018