Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning. - Francesca Bartolucci; Emmanuel de Bézenac; Bogdan Raonic; Roberto Molinaro; Siddhartha Mishra; Rima Alaifari (NeurIPS, 2023)

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)

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

Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning. - Francesca Bartolucci; Emmanuel de Bézenac; Bogdan Raonic; Roberto Molinaro; Siddhartha Mishra; Rima Alaifari (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)

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)

RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains. - Sepehr Mousavi; Shizheng Wen; Levi E. Lingsch; Maximilian Herde; Bogdan Raonic; Siddhartha Mishra (CoRR, 2025)