Statistical solutions of the incompressible Euler equations. - Samuel Lanthaler; Siddhartha Mishra; Carlos Parés-Pulido (CoRR, 2019)

On the conservation of energy in two-dimensional incompressible flows. - Samuel Lanthaler; Siddhartha Mishra; Carlos Parés-Pulido (CoRR, 2020)

On concentration in vortex sheets. - Samuel Lanthaler (CoRR, 2020)

On the Convergence of the Spectral Viscosity Method for the Two-Dimensional Incompressible Euler Equations with Rough Initial Data. - Samuel Lanthaler; Siddhartha Mishra (Found. Comput. Math., 2020)

Error estimates for DeepOnets: A deep learning framework in infinite dimensions. - Samuel Lanthaler; Siddhartha Mishra; George Em Karniadakis (CoRR, 2021)

On the well-posedness of Bayesian inversion for PDEs with ill-posed forward problems. - Samuel Lanthaler; Siddhartha Mishra; Franziska Weber (CoRR, 2021)

On Universal Approximation and Error Bounds for Fourier Neural Operators. - Nikola B. Kovachki; Samuel Lanthaler; Siddhartha Mishra (J. Mach. Learn. Res., 2021)

On the approximation of functions by tanh neural networks. - Tim De Ryck; Samuel Lanthaler; Siddhartha Mishra (Neural Networks, 2021)

Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. - Samuel Lanthaler; Roberto Molinaro; Patrik Hadorn; Siddhartha Mishra (ICLR, 2023)

Error Bounds for Learning with Vector-Valued Random Features. - Samuel Lanthaler; Nicholas H. Nelsen (NeurIPS, 2023)

Neural Oscillators are Universal. - Samuel Lanthaler; T. Konstantin Rusch; Siddhartha Mishra (NeurIPS, 2023)

The Nonlocal Neural Operator: Universal Approximation. - Samuel Lanthaler; Zongyi Li; Andrew M. Stuart (CoRR, 2023)

The curse of dimensionality in operator learning. - Samuel Lanthaler; Andrew M. Stuart (CoRR, 2023)

Operator learning with PCA-Net: upper and lower complexity bounds. - Samuel Lanthaler (J. Mach. Learn. Res., 2023)

Operator Learning: Algorithms and Analysis. - Nikola B. Kovachki; Samuel Lanthaler; Andrew M. Stuart (CoRR, 2024)

Discretization Error of Fourier Neural Operators. - Samuel Lanthaler; Andrew M. Stuart; Margaret Trautner (CoRR, 2024)

Data Complexity Estimates for Operator Learning. - Nikola B. Kovachki; Samuel Lanthaler; Hrushikesh Mhaskar (CoRR, 2024)

Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective. - Samuel Lanthaler (CoRR, 2024)

Generative AI for fast and accurate Statistical Computation of Fluids. - Roberto Molinaro; Samuel Lanthaler; Bogdan Raonic; Tobias Rohner; Victor Armegioiu; Zhong Yi Wan; Fei Sha; Siddhartha Mishra; Leonardo Zepeda-Núñez (CoRR, 2024)

Theory-to-Practice Gap for Neural Networks and Neural Operators. - Philipp Grohs; Samuel Lanthaler; Margaret Trautner (CoRR, 2025)

Scale-Consistent Learning for Partial Differential Equations. - Zongyi Li; Samuel Lanthaler; Catherine Deng; Michael Chen; Yixuan Wang; Kamyar Azizzadenesheli; Anima Anandkumar (CoRR, 2025)

Merging Memory and Space: A Spatiotemporal State Space Neural Operator. - Nodens F. Koren; Samuel Lanthaler (CoRR, 2025)