Convolutional Neural Operators for robust and accurate learning of PDEs. - Bogdan Raonic; Roberto Molinaro; Tim De Ryck; Tobias Rohner; Francesca Bartolucci; Rima Alaifari; Siddhartha Mishra; Emmanuel de Bézenac (NeurIPS, 2023)

Approximate Reversible Circuits for NISQ-Era Quantum Computers. - Niels Gleinig; Tobias Rohner; Torsten Hoefler (CoRR, 2023)

Convolutional Neural Operators. - Bogdan Raonic; Roberto Molinaro; Tobias Rohner; Siddhartha Mishra; Emmanuel de Bézenac (CoRR, 2023)

Poseidon: Efficient Foundation Models for PDEs. - Maximilian Herde; Bogdan Raonic; Tobias Rohner; Roger Käppeli; Roberto Molinaro; Emmanuel de Bézenac; Siddhartha Mishra (NeurIPS, 2024)

Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows. - Tobias Rohner; Siddhartha Mishra (PASC, 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)

Sufficient Conditions for the Energy Balance for the Stochastic Incompressible Euler Equations with Additive Noise in two Space Dimensions. - Tobias Rohner; Franziska Weber (CoRR, 2025)