Mishra, Siddhartha

25 publications

NeurIPS 2025 Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains Shizheng Wen, Arsh Kumbhat, Levi Lingsch, Sepehr Mousavi, Yizhou Zhao, Praveen Chandrashekar, Siddhartha Mishra

NeurIPS 2025 HyPINO: Multi-Physics Neural Operators via HyperPINNs and the Method of Manufactured Solutions Rafael Bischof, Michal Piovarci, Michael Anton Kraus, Siddhartha Mishra, Bernd Bickel

NeurIPS 2025 RIGNO: A Graph-Based Framework for Robust and Accurate Operator Learning for PDEs on Arbitrary Domains Sepehr Mousavi, Shizheng Wen, Levi Lingsch, Maximilian Herde, Bogdan Raonic, Siddhartha Mishra

ICLR 2024 An Operator Preconditioning Perspective on Training in Physics-Informed Machine Learning Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bezenac

ICML 2024 Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains Levi E. Lingsch, Mike Yan Michelis, Emmanuel De Bezenac, Sirani M. Perera, Robert K. Katzschmann, Siddhartha Mishra

NeurIPS 2024 FUSE: Fast Unified Simulation and Estimation for PDEs Levi E. Lingsch, Dana Grund, Siddhartha Mishra, Georgios Kissas

TMLR 2024 How Does Over-Squashing Affect the Power of GNNs? Francesco Di Giovanni, T. Konstantin Rusch, Michael Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Veličković

NeurIPS 2024 Poseidon: Efficient Foundation Models for PDEs Maximilian Herde, Bogdan Raonić, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra

NeurIPS 2024 SmallToLarge (S2L): Scalable Data Selection for Fine-Tuning Large Language Models by Summarizing Training Trajectories of Small Models Yu Yang, Siddhartha Mishra, Jeffrey Chiang, Baharan Mirzasoleiman

ICLRW 2023 Convolutional Neural Operators Bogdan Raonic, Roberto Molinaro, Tobias Rohner, Siddhartha Mishra, Emmanuel de Bezenac

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

ICLR 2023 Gradient Gating for Deep Multi-Rate Learning on Graphs T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra

NeurIPSW 2023 How Does Over-Squashing Affect the Power of GNNs? Francesco Di Giovanni, T. Konstantin Rusch, Michael Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Veličković

ICLRW 2023 Multi-Scale Message Passing Neural PDE Solvers Léonard Equer, T. Konstantin Rusch, Siddhartha Mishra

ICML 2023 Neural Inverse Operators for Solving PDE Inverse Problems Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra

NeurIPS 2023 Neural Oscillators Are Universal Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra

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

NeurIPS 2023 Representation Equivalent Neural Operators: A Framework for Alias-Free Operator Learning Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari

AAAI 2022 An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity Siddhartha Mishra, Nicholas Monath, Michael Boratko, Ariel Kobren, Andrew McCallum

NeurIPS 2022 Generic Bounds on the Approximation Error for Physics-Informed (and) Operator Learning Tim De Ryck, Siddhartha Mishra

ICML 2022 Graph-Coupled Oscillator Networks T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael Bronstein

ICLR 2022 Long Expressive Memory for Sequence Modeling T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney

ICLR 2021 Coupled Oscillatory Recurrent Neural Network (coRNN): An Accurate and (gradient) Stable Architecture for Learning Long Time Dependencies T. Konstantin Rusch, Siddhartha Mishra

JMLR 2021 On Universal Approximation and Error Bounds for Fourier Neural Operators Nikola Kovachki, Samuel Lanthaler, Siddhartha Mishra

ICML 2021 UnICORNN: A Recurrent Model for Learning Very Long Time Dependencies T. Konstantin Rusch, Siddhartha Mishra