Philippe von Wurstemberger

Postdoc, School of Data Science, The Chinese University of Hong Kong

Joined

January 2026

Names

Philippe von Wurstemberger (Preferred)

Emails

****@cuhk.edu.cn (Confirmed)

,

****@math.ethz.ch

Personal Links

Career & Education History

Postdoc

School of Data Science, The Chinese University of Hong Kong (cuhk.edu.cn)

2024 – Present

PhD student

ETHZ - ETH Zurich (ethz.ch)

2018 – 2024

MS student

ETHZ - ETH Zurich (ethz.ch)

2016 – 2018

Undergrad student

Mathematics, ETHZ - ETH Zurich (ethz.ch)

2012 – 2016

Advisors, Relations & Conflicts

Coauthor

Present

Postdoc Advisor

2024 – Present

PhD Advisor

Patrick Cheridito

2020 – 2024

PhD Advisor

2018 – 2024

Expertise

Scientific machine learning

2018 – Present

Deep learning

2018 – Present

Publications

Adam symmetry theorem: characterization of the convergence of the stochastic Adam optimizer
Steffen Dereich, Thang Do, Arnulf Jentzen, Philippe von Wurstemberger
- CoRR 2025
- Readers: Everyone
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
Lukas Gonon, Arnulf Jentzen, Benno Kuckuck, Siyu Liang, Adrian Riekert, Philippe von Wurstemberger
- CoRR 2024
- Readers: Everyone
An overview of diffusion models for generative artificial intelligence
Davide Gallon, Arnulf Jentzen, Philippe von Wurstemberger
- CoRR 2024
- Readers: Everyone
Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger
- CoRR 2023
- Readers: Everyone
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
- Readers: Everyone
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
- Readers: Everyone
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
- Readers: Everyone
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
- Readers: Everyone
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
- Readers: Everyone
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
- Readers: Everyone

Co-Authors