Philippe von Wurstemberger

According to our database1, Philippe von Wurstemberger authored at least 8 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2024
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning.
CoRR, 2024

2023
Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory.
CoRR, 2023

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations.
CoRR, 2023

2022
Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing.
CoRR, 2022

2020
Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates.
J. Complex., 2020

High-dimensional approximation spaces of artificial neural networks and applications to partial differential equations.
CoRR, 2020

Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations.
CoRR, 2020

2018
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations.
CoRR, 2018


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