Chaotic Hedging with Iterated Integrals and Neural Networks. - Ariel Neufeld; Philipp Schmocker (CoRR, 2022)

Global universal approximation of functional input maps on weighted spaces. - Christa Cuchiero; Philipp Schmocker; Josef Teichmann (CoRR, 2023)

Universal Approximation Property of Random Neural Networks. - Ariel Neufeld; Philipp Schmocker (CoRR, 2023)

Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs with infinite activity. - Ariel Neufeld; Philipp Schmocker; Sizhou Wu (CoRR, 2024)

Universal approximation results for neural networks with non-polynomial activation function over non-compact domains. - Ariel Neufeld; Philipp Schmocker (CoRR, 2024)

Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion. - Ariel Neufeld; Philipp Schmocker (CoRR, 2024)

Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs. - Ariel Neufeld; Philipp Schmocker; Sizhou Wu (Commun. Nonlinear Sci. Numer. Simul., 2025)

Multilevel Picard approximations for McKean-Vlasov stochastic differential equations with nonconstant diffusion. - Ariel Neufeld; Tuan Anh Nguyen; Philipp Schmocker (CoRR, 2025)