Philippe von Wurstemberger
Philippe von Wurstemberger
Verified email at sam.math.ethz.ch - Homepage
TitleCited byYear
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
P Grohs, F Hornung, A Jentzen, P Von Wurstemberger
arXiv preprint arXiv:1809.02362, 2018
232018
Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations
M Hutzenthaler, A Jentzen, T Kruse, TA Nguyen, P von Wurstemberger
arXiv preprint arXiv:1807.01212, 2018
102018
Strong error analysis for stochastic gradient descent optimization algorithms
A Jentzen, B Kuckuck, A Neufeld, P von Wurstemberger
arXiv preprint arXiv:1801.09324, 2018
52018
Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks
M Hutzenthaler, A Jentzen, P von Wurstemberger
arXiv preprint arXiv:1903.05985, 2019
32019
Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates
A Jentzen, P Von Wurstemberger
arXiv preprint arXiv:1803.08600, 2018
12018
Overcoming the course of dimensionality with DNNs: Theoretical approximation results for PDEs
P von Wurstemberger
3rd International Conference on Computational Finance (ICCF2019), 86, 0
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Articles 1–6