Showing 1–14 of 14 results
/ Date/ Name
Dec 8, 2020High-dimensional approximation spaces of artificial neural networks and applications to partial differential equationsMar 22, 2018Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning ratesFeb 6, 2022Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricingDec 2, 2024An overview of diffusion models for generative artificial intelligenceAug 23, 2024An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator LearningFeb 10, 2026Physics-informed diffusion models in spectral spaceMay 20, 2020Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equationsMar 14, 2019Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risksFeb 7, 2023Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equationsOct 31, 2023Mathematical Introduction to Deep Learning: Methods, Implementations, and TheorySep 7, 2018A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equationsJan 29, 2018Strong error analysis for stochastic gradient descent optimization algorithmsJul 3, 2018Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equationsNov 10, 2025Adam symmetry theorem: characterization of the convergence of the stochastic Adam optimizer