List of publications

Preprints

• P. Schmocker, J. Teichmann. Weighted universal approximation of differentiable maps on infinite-dimensional manifolds, 2026.
• A. Neufeld, P. Schmocker, V. K. Tran. Approximation rates of quantum neural networks for periodic functions via Jackson's inequality, 2025.
• A. Kratsios, A. Neufeld, P. Schmocker. Generative neural operators of log-complexity can simultaneously solve infinitely many convex programs, 2025.
• A. Neufeld, P. Schmocker. Solving stochastic partial differential equations with neural networks in the Wiener chaos expansion, 2024.

Publications

• A. Neufeld, P. Schmocker. Universal approximation property of Banach space-valued random feature models including random neural networks, accepted for publication in The Annals of Applied Probability, 2026.
• A. Neufeld, T. A. Nguyen, P. Schmocker. Multilevel Picard approximations for McKean-Vlasov stochastic differential equations with nonconstant diffusion parts, accepted for publication in IMA Journal of Numerical Analysis, 2026.
• A. Neufeld, P. Schmocker. Chaotic hedging with iterated integrals and neural networks, accepted for publication in Finance and Stochastics, 2026.
• C. Cuchiero, P. Schmocker, J. Teichmann. Global universal approximation of functional input maps on weighted spaces, Constructive Approximation, Vol. 63, pp. 537-612, 2026.
• A. Neufeld, P. Schmocker. Universal approximation results for neural networks with non-polynomial activation function over non-compact domains, Analysis and Applications, Vol. 24, No. 05, pp. 1123-1173, 2026.
• A. Neufeld, P. Schmocker, S. Wu. Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs, Communications in Nonlinear Science and Numerical Simulation, Vol. 143, pp. 108556, 2025.

PhD-Theses

• P. Schmocker. Random neural networks and Wiener chaos expansion, Nanyang Technological University (NTU), Singapore, 2025.
• P. Schmocker. Universal approximation on path spaces and applications in finance, University of St.Gallen.