Philippe von Wurstemberger

11 papers Journal 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Physics-informed diffusion models in spectral space. CoRR Davide Gallon, Philippe von Wurstemberger, Patrick Cheridito, Arnulf Jentzen
2025	J	`jnl`	Adam symmetry theorem: characterization of the convergence of the stochastic Adam optimizer. CoRR Steffen Dereich, Thang Do, Arnulf Jentzen, Philippe von Wurstemberger
2024	J	`jnl`	An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning. CoRR Lukas Gonon, Arnulf Jentzen, Benno Kuckuck, Siyu Liang, Adrian Riekert, Philippe von Wurstemberger
2024	J	`jnl`	An overview of diffusion models for generative artificial intelligence. CoRR Davide Gallon, Arnulf Jentzen, Philippe von Wurstemberger
2023	J	`jnl`	Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations. CoRR Arnulf Jentzen, Adrian Riekert, Philippe von Wurstemberger
2023	J	`jnl`	Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory. CoRR Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger
2022	J	`jnl`	Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing. CoRR Sebastian Becker, Arnulf Jentzen, Marvin S. Müller, Philippe von Wurstemberger
2020	J	`jnl`	High-dimensional approximation spaces of artificial neural networks and applications to partial differential equations. CoRR Pierfrancesco Beneventano, Patrick Cheridito, Arnulf Jentzen, Philippe von Wurstemberger
2020	J	`jnl`	Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates. J. Complex. Arnulf Jentzen, Philippe von Wurstemberger
2020	J	`jnl`	Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations. CoRR Sebastian Becker, Ramon Braunwarth, Martin Hutzenthaler, Arnulf Jentzen, Philippe von Wurstemberger
2018	J	`jnl`	A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations. CoRR Philipp Grohs, Fabian Hornung, Arnulf Jentzen, Philippe von Wurstemberger