A Posteriori Error Estimation of hp-dG Finite Element Methods for Highly Indefinite Helmholtz Problems. - Stefan A. Sauter; Jakob Zech (SIAM J. Numer. Anal., 2015)

Shape Holomorphy of the Stationary Navier-Stokes Equations. - Albert Cohen 002; Christoph Schwab; Jakob Zech (SIAM J. Math. Anal., 2018)

Uncertainty Quantification for Spectral Fractional Diffusion: Sparsity Analysis of Parametric Solutions. - Lukas Herrmann; Christoph Schwab; Jakob Zech (SIAM/ASA J. Uncertain. Quantification, 2019)

Sparse approximation of triangular transports on bounded domains. - Jakob Zech; Youssef M. Marzouk (CoRR, 2020)

Domain Uncertainty Quantification in Computational Electromagnetics. - Rubén Aylwin; Carlos Jerez-Hanckes; Christoph Schwab; Jakob Zech (SIAM/ASA J. Uncertain. Quantification, 2020)

Sparse approximation of triangular transports. Part II: the infinite dimensional case. - Jakob Zech; Youssef M. Marzouk (CoRR, 2021)

Deep Learning in High Dimension: Neural Network Approximation of Analytic Functions in L2(Rd, γd). - Christoph Schwab; Jakob Zech (CoRR, 2021)

Multilevel Optimization for Inverse Problems. - Simon Weissmann; Ashia Wilson; Jakob Zech (COLT, 2022)

Analyticity and sparsity in uncertainty quantification for PDEs with Gaussian random field inputs. - Dinh Dung; Van Kien Nguyen; Christoph Schwab; Jakob Zech (CoRR, 2022)

De Rham compatible Deep Neural Networks. - Marcello Longo; Joost A. A. Opschoor; Nico Disch; Christoph Schwab; Jakob Zech (CoRR, 2022)

Neural and gpc operator surrogates: construction and expression rate bounds. - Lukas Herrmann; Christoph Schwab; Jakob Zech (CoRR, 2022)

Multilevel Domain Uncertainty Quantification in Computational Electromagnetics. - Rubén Aylwin; Carlos Jerez-Hanckes; Christoph Schwab; Jakob Zech (CoRR, 2022)

Deep Operator Network Approximation Rates for Lipschitz Operators. - Christoph Schwab; Andreas Stein; Jakob Zech (CoRR, 2023)

Measure transport via polynomial density surrogates. - Josephine Westermann; Jakob Zech (CoRR, 2023)

Deep Learning in High Dimension: Neural Network Expression Rates for Analytic Functions in ( pmb{L^2( mathbb{R}^d, gamma_d)} ). - Christoph Schwab; Jakob Zech (SIAM/ASA J. Uncertain. Quantification, 2023)

De Rham compatible Deep Neural Network FEM. - Marcello Longo; Joost A. A. Opschoor; Nico Disch; Christoph Schwab; Jakob Zech (Neural Networks, 2023)

On the mean-field limit for Stein variational gradient descent: stability and multilevel approximation. - Simon Weissmann; Jakob Zech (CoRR, 2024)

Mathematical theory of deep learning. - Philipp Petersen; Jakob Zech (CoRR, 2024)

Statistical Learning Theory for Neural Operators. - Niklas Reinhardt; Sven Wang; Jakob Zech (CoRR, 2024)

Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective. - Youssef M. Marzouk; Zhi (Robert) Ren; Sven Wang; Jakob Zech (J. Mach. Learn. Res., 2024)

Neural and spectral operator surrogates: unified construction and expression rate bounds. - Lukas Herrmann; Christoph Schwab; Jakob Zech (Adv. Comput. Math., 2024)

Optimal Scheduling of Dynamic Transport. - Panos Tsimpos; Zhi Ren 003; Jakob Zech; Youssef Marzouk 001 (COLT, 2025)

Distribution learning via neural differential equations: minimal energy regularization and approximation theory. - Youssef M. Marzouk; Zhi Ren 003; Jakob Zech (CoRR, 2025)

Low Stein Discrepancy via Message-Passing Monte Carlo. - Nathan Kirk; T. Konstantin Rusch; Jakob Zech; Daniela Rus (CoRR, 2025)

Sparsity for Infinite-Parametric Holomorphic Functions on Gaussian Spaces. - Carlo Marcati; Christoph Schwab; Jakob Zech (CoRR, 2025)

Quantum Circuit Encodings of Polynomial Chaos Expansions. - Junaid Aftab; Christoph Schwab; Haizhao Yang; Jakob Zech (CoRR, 2025)

Metropolis-adjusted interacting particle sampling. - Björn Sprungk; Simon Weissmann; Jakob Zech (Stat. Comput., 2025)