Siddhartha Mishra

ORCID

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 86

Top Venues

Publications

Levi E. Lingsch, Dana Grund, Siddhartha Mishra, Georgios Kissas
FUSE: Fast Unified Simulation and Estimation for PDEs. CoRR (2024)
Maximilian Herde, Bogdan Raonic, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra
Poseidon: Efficient Foundation Models for PDEs. CoRR (2024)
Ulrik Skre Fjordholm, Siddhartha Mishra, Franziska Weber
On the Vanishing Viscosity Limit of Statistical Solutions of the Incompressible Navier-Stokes Equations. SIAM J. Math. Anal. 56 (4) (2024)
Yu Yang, Siddhartha Mishra, Jeffrey N. Chiang, Baharan Mirzasoleiman
SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models. CoRR (2024)
Tobias Rohner, Siddhartha Mishra
Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows. PASC (2024)
Tim De Ryck, Siddhartha Mishra, Roberto Molinaro
wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws. SIAM J. Numer. Anal. 62 (2) (2024)
Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic
How does over-squashing affect the power of GNNs? Trans. Mach. Learn. Res. 2024 (2024)
Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
An operator preconditioning perspective on training in physics-informed machine learning. ICLR (2024)
Tim De Ryck, Siddhartha Mishra
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning. CoRR (2024)
Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra
Neural Inverse Operators for Solving PDE Inverse Problems. ICML (2023)
Marco Petrella, Rémi Abgrall, Siddhartha Mishra
A Monte-Carlo ab-initio algorithm for the multiscale simulation of compressible multiphase flows. CoRR (2023)
Victorita Dolean, Alexander Heinlein, Siddhartha Mishra, Ben Moseley
Multilevel domain decomposition-based architectures for physics-informed neural networks. CoRR (2023)
Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning. NeurIPS (2023)
T. Konstantin Rusch, Michael M. Bronstein, Siddhartha Mishra
A Survey on Oversmoothing in Graph Neural Networks. CoRR (2023)
Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari
Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning. CoRR (2023)
Léonard Equer, T. Konstantin Rusch, Siddhartha Mishra
Multi-Scale Message Passing Neural PDE Solvers. CoRR (2023)
T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
Gradient Gating for Deep Multi-Rate Learning on Graphs. ICLR (2023)
Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. ICLR (2023)
Bogdan Raonic, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, Emmanuel de Bézenac
Convolutional Neural Operators for robust and accurate learning of PDEs. NeurIPS (2023)
Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra
Neural Inverse Operators for Solving PDE Inverse Problems. CoRR (2023)
Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic
How does over-squashing affect the power of GNNs? CoRR (2023)
Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
Neural Oscillators are Universal. NeurIPS (2023)
Bogdan Raonic, Roberto Molinaro, Tobias Rohner, Siddhartha Mishra, Emmanuel de Bézenac
Convolutional Neural Operators. CoRR (2023)
Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
An operator preconditioning perspective on training in physics-informed machine learning. CoRR (2023)
Benjamin Scellier, Siddhartha Mishra
A universal approximation theorem for nonlinear resistive networks. CoRR (2023)
Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
Neural Oscillators are Universal. CoRR (2023)
Marco Petrella, Rémi Abgrall, Siddhartha Mishra
On the discrete equation model for compressible multiphase fluid flows. J. Comput. Phys. 478 (2023)
Tim De Ryck, Siddhartha Mishra
Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs. Adv. Comput. Math. 48 (6) (2022)
Tim De Ryck, Siddhartha Mishra
Generic bounds on the approximation error for physics-informed (and) operator learning. CoRR (2022)
T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney
Long Expressive Memory for Sequence Modeling. ICLR (2022)
Tim De Ryck, Siddhartha Mishra
Generic bounds on the approximation error for physics-informed (and) operator learning. NeurIPS (2022)
Shib Sankar Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, Andrew McCallum
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings. ACL (1) (2022)
T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
Gradient Gating for Deep Multi-Rate Learning on Graphs. CoRR (2022)
Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. CoRR (2022)
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Hannaneh Hajishirzi, Noah A. Smith, Daniel Khashabi
Benchmarking Generalization via In-Context Instructions on 1, 600+ Language Tasks. CoRR (2022)
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks. EMNLP (2022)
T. Konstantin Rusch, Benjamin Paul Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein
Graph-Coupled Oscillator Networks. CoRR (2022)
T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein
Graph-Coupled Oscillator Networks. ICML (2022)
Tim De Ryck, Siddhartha Mishra
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws. CoRR (2022)
Tim De Ryck, Siddhartha Mishra, Roberto Molinaro
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws. CoRR (2022)
Siddhartha Mishra, Nicholas Monath, Michael Boratko, Ariel Kobren, Andrew McCallum
An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity. AAAI (2022)
Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra
Error estimates for physics informed neural networks approximating the Navier-Stokes equations. CoRR (2022)
Marco Petrella, Rémi Abgrall, Siddhartha Mishra
On the discrete equation model for compressible multiphase fluid flows. CoRR (2022)
Benjamin Scellier, Siddhartha Mishra, Yoshua Bengio, Yann Ollivier
Agnostic Physics-Driven Deep Learning. CoRR (2022)
Victorita Dolean, Alexander Heinlein, Siddhartha Mishra, Ben Moseley
Finite basis physics-informed neural networks as a Schwarz domain decomposition method. CoRR (2022)
Michael Prasthofer, Tim De Ryck, Siddhartha Mishra
Variable-Input Deep Operator Networks. CoRR (2022)
Tim De Ryck, Siddhartha Mishra
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs. CoRR (2021)
Nikola Kovachki, Samuel Lanthaler, Siddhartha Mishra
On universal approximation and error bounds for Fourier Neural Operators. CoRR (2021)
Siddhartha Mishra, David Ochsner, Adrian M. Ruf, Franziska Weber
Well-posedness of Bayesian inverse problems for hyperbolic conservation laws. CoRR (2021)
Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra
On the approximation of functions by tanh neural networks. Neural Networks 143 (2021)
Marcello Longo, Siddhartha Mishra, T. Konstantin Rusch, Christoph Schwab
Higher-Order Quasi-Monte Carlo Training of Deep Neural Networks. SIAM J. Sci. Comput. 43 (6) (2021)
T. Konstantin Rusch, Siddhartha Mishra
UnICORNN: A recurrent model for learning very long time dependencies. ICML (2021)
Siddhartha Mishra, T. Konstantin Rusch
Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences. SIAM J. Numer. Anal. 59 (3) (2021)
Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra
On the approximation of functions by tanh neural networks. CoRR (2021)
Nikola Kovachki, Samuel Lanthaler, Siddhartha Mishra
On Universal Approximation and Error Bounds for Fourier Neural Operators. J. Mach. Learn. Res. 22 (2021)
T. Konstantin Rusch, Siddhartha Mishra
UnICORNN: A recurrent model for learning very long time dependencies. CoRR (2021)
Samuel Lanthaler, Siddhartha Mishra, George Em Karniadakis
Error estimates for DeepOnets: A deep learning framework in infinite dimensions. CoRR (2021)
Genming Bai, Ujjwal Koley, Siddhartha Mishra, Roberto Molinaro
Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs. CoRR (2021)
T. Konstantin Rusch, Siddhartha Mishra
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies. ICLR (2021)
T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney
Long Expressive Memory for Sequence Modeling. CoRR (2021)
Samuel Lanthaler, Siddhartha Mishra, Franziska Weber
On the well-posedness of Bayesian inversion for PDEs with ill-posed forward problems. CoRR (2021)
Gioele Janett, Oskar Steiner, Ernest Alsina Ballester, Luca Belluzzi, Siddhartha Mishra
A novel fourth-order WENO interpolation technique. A possible new tool designed for radiative transfer. CoRR (2021)
Siddhartha Mishra, Roberto Molinaro
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs II: A class of inverse problems. CoRR (2020)
T. Konstantin Rusch, Siddhartha Mishra
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies. CoRR (2020)
Kjetil O. Lye, Siddhartha Mishra, Deep Ray
Deep learning observables in computational fluid dynamics. J. Comput. Phys. 410 (2020)
Siddhartha Mishra, Roberto Molinaro
Physics Informed Neural Networks for Simulating Radiative Transfer. CoRR (2020)
Siddhartha Mishra, Roberto Molinaro
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs. CoRR (2020)
Samuel Lanthaler, Siddhartha Mishra, Carlos Parés-Pulido
On the conservation of energy in two-dimensional incompressible flows. CoRR (2020)
Siddhartha Mishra, T. Konstantin Rusch
Enhancing accuracy of deep learning algorithms by training with low-discrepancy sequences. CoRR (2020)
Marcello Longo, Siddhartha Mishra, T. Konstantin Rusch, Christoph Schwab
Higher-order Quasi-Monte Carlo Training of Deep Neural Networks. CoRR (2020)
Kjetil O. Lye, Siddhartha Mishra, Deep Ray, Praveen Chandrasekhar
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks. CoRR (2020)
Samuel Lanthaler, Siddhartha Mishra
On the Convergence of the Spectral Viscosity Method for the Two-Dimensional Incompressible Euler Equations with Rough Initial Data. Found. Comput. Math. 20 (5) (2020)
Kjetil O. Lye, Siddhartha Mishra, Deep Ray
Deep learning observables in computational fluid dynamics. CoRR (2019)
Ulrik Skre Fjordholm, Kjetil O. Lye, Siddhartha Mishra, Franziska Weber
Statistical solutions of hyperbolic systems of conservation laws: numerical approximation. CoRR (2019)
Samuel Lanthaler, Siddhartha Mishra, Carlos Parés-Pulido
Statistical solutions of the incompressible Euler equations. CoRR (2019)
Kjetil O. Lye, Siddhartha Mishra, Roberto Molinaro
A Multi-level procedure for enhancing accuracy of machine learning algorithms. CoRR (2019)
Tim De Ryck, Siddhartha Mishra, Deep Ray
On the approximation of rough functions with deep neural networks. CoRR (2019)
Divya Spoorthy, Shanmukh Reddy Manne, Vaibhav Dhyani, Sarpras Swain, Shahna Shahulhameed, Siddhartha Mishra, Inderjeet Kaur, Lopamudra Giri, Soumya Jana
Automatic Identification of Mixed Retinal Cells in Time-Lapse Fluorescent Microscopy Images using High-Dimensional DBSCAN. EMBC (2019)
Ulrik S. Fjordholm, Kjetil O. Lye, Siddhartha Mishra
Numerical Approximation of Statistical Solutions of Scalar Conservation Laws. SIAM J. Numer. Anal. 56 (5) (2018)
Siddhartha Mishra
A machine learning framework for data driven acceleration of computations of differential equations. CoRR (2018)
Ulrik S. Fjordholm, Roger Käppeli, Siddhartha Mishra, Eitan Tadmor
Construction of Approximate Entropy Measure-Valued Solutions for Hyperbolic Systems of Conservation Laws. Found. Comput. Math. 17 (3) (2017)
Siddhartha Mishra, Nils Henrik Risebro, Christoph Schwab, Svetlana V. Tokareva
Numerical Solution of Scalar Conservation Laws with Random Flux Functions. SIAM/ASA J. Uncertain. Quantification 4 (1) (2016)
Giuseppe Maria Coclite, M. M. Coclite, Siddhartha Mishra
On a Model for the Evolution of Morphogens in a Growing Tissue. SIAM J. Math. Anal. 48 (3) (2016)
Siddhartha Mishra, Christoph Schwab, Jonas Sukys
Multi-level Monte Carlo finite volume methods for uncertainty quantification of acoustic wave propagation in random heterogeneous layered medium. J. Comput. Phys. 312 (2016)
Andreas Hiltebrand, Siddhartha Mishra
Entropy stability and well-balancedness of space-time DG for the shallow water equations with bottom topography. Networks Heterog. Media 11 (1) (2016)
Ulrik S. Fjordholm, Siddhartha Mishra, Eitan Tadmor
On the computation of measure-valued solutions. Acta Numer. 25 (2016)
Jan Ernest, Philippe G. LeFloch, Siddhartha Mishra
Schemes with Well-Controlled Dissipation. SIAM J. Numer. Anal. 53 (1) (2015)
Andreas Hiltebrand, Siddhartha Mishra
Entropy stable shock capturing space-time discontinuous Galerkin schemes for systems of conservation laws. Numerische Mathematik 126 (1) (2014)
Philippe G. LeFloch, Siddhartha Mishra
Numerical methods with controlled dissipation for small-scale dependent shocks. Acta Numer. 23 (2014)
Manuel J. Castro, Ulrik S. Fjordholm, Siddhartha Mishra, Carlos Parés Madroñal
Entropy Conservative and Entropy Stable Schemes for Nonconservative Hyperbolic Systems. SIAM J. Numer. Anal. 51 (3) (2013)
Giuseppe Maria Coclite, Lorenzo di Ruvo, Jan Ernest, Siddhartha Mishra
Convergence of vanishing capillarity approximations for scalar conservation laws with discontinuous fluxes. Networks Heterog. Media 8 (4) (2013)
Ulrik S. Fjordholm, Siddhartha Mishra, Eitan Tadmor
ENO Reconstruction and ENO Interpolation Are Stable. Found. Comput. Math. 13 (2) (2013)
Siddhartha Mishra, Christoph Schwab, Jonas Sukys
Multilevel Monte Carlo Finite Volume Methods for Shallow Water Equations with Uncertain Topography in Multi-dimensions. SIAM J. Sci. Comput. 34 (6) (2012)
Siddhartha Mishra, Christoph Schwab
Sparse tensor multi-level Monte Carlo finite volume methods for hyperbolic conservation laws with random initial data. Math. Comput. 81 (280) (2012)
Siddhartha Mishra, Christoph Schwab, Jonas Sukys
Multi-level Monte Carlo finite volume methods for nonlinear systems of conservation laws in multi-dimensions. J. Comput. Phys. 231 (8) (2012)
Ulrik S. Fjordholm, Siddhartha Mishra, Eitan Tadmor
Arbitrarily High-order Accurate Entropy Stable Essentially Nonoscillatory Schemes for Systems of Conservation Laws. SIAM J. Numer. Anal. 50 (2) (2012)
Harish Kumar, Siddhartha Mishra
Entropy Stable Numerical Schemes for Two-Fluid Plasma Equations. J. Sci. Comput. 52 (2) (2012)
Ulrik S. Fjordholm, Siddhartha Mishra, Eitan Tadmor
Well-balanced and energy stable schemes for the shallow water equations with discontinuous topography. J. Comput. Phys. 230 (14) (2011)
Siddhartha Mishra, Eitan Tadmor
Constraint Preserving Schemes Using Potential-Based Fluxes. II. Genuinely Multidimensional Systems of Conservation Laws. SIAM J. Numer. Anal. 49 (3) (2011)
Jonas Sukys, Siddhartha Mishra, Christoph Schwab
Static Load Balancing for Multi-level Monte Carlo Finite Volume Solvers. PPAM (1) (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Siddhartha Mishra