Siddhartha Mishra

Orcid: 0000-0002-2665-5385

According to our database1, Siddhartha Mishra authored at least 96 papers between 2005 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
How does over-squashing affect the power of GNNs?
Trans. Mach. Learn. Res., 2024

wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws.
SIAM J. Numer. Anal., 2024

On the Vanishing Viscosity Limit of Statistical Solutions of the Incompressible Navier-Stokes Equations.
SIAM J. Math. Anal., 2024

S7: Selective and Simplified State Space Layers for Sequence Modeling.
CoRR, 2024

Generative AI for fast and accurate Statistical Computation of Fluids.
CoRR, 2024

Poseidon: Efficient Foundation Models for PDEs.
CoRR, 2024

FUSE: Fast Unified Simulation and Estimation for PDEs.
CoRR, 2024

SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models.
CoRR, 2024

Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning.
Acta Numer., 2024

Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows.
Proceedings of the Platform for Advanced Scientific Computing Conference, 2024

Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

An operator preconditioning perspective on training in physics-informed machine learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
On the discrete equation model for compressible multiphase fluid flows.
J. Comput. Phys., April, 2023

A universal approximation theorem for nonlinear resistive networks.
CoRR, 2023

Multilevel domain decomposition-based architectures for physics-informed neural networks.
CoRR, 2023

Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning.
CoRR, 2023

Vandermonde Neural Operators.
CoRR, 2023

A Monte-Carlo ab-initio algorithm for the multiscale simulation of compressible multiphase flows.
CoRR, 2023

A Survey on Oversmoothing in Graph Neural Networks.
CoRR, 2023

Multi-Scale Message Passing Neural PDE Solvers.
CoRR, 2023

Convolutional Neural Operators.
CoRR, 2023

Convolutional Neural Operators for robust and accurate learning of PDEs.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Oscillators are Universal.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Inverse Operators for Solving PDE Inverse Problems.
Proceedings of the International Conference on Machine Learning, 2023

Gradient Gating for Deep Multi-Rate Learning on Graphs.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Finite basis physics-informed neural networks as a Schwarz domain decomposition method.
CoRR, 2022

Error analysis for deep neural network approximations of parametric hyperbolic conservation laws.
CoRR, 2022

Agnostic Physics-Driven Deep Learning.
CoRR, 2022

Variable-Input Deep Operator Networks.
CoRR, 2022

Benchmarking Generalization via In-Context Instructions on 1, 600+ Language Tasks.
CoRR, 2022

Error estimates for physics informed neural networks approximating the Navier-Stokes equations.
CoRR, 2022

Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs.
Adv. Comput. Math., 2022

Generic bounds on the approximation error for physics-informed (and) operator learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Graph-Coupled Oscillator Networks.
Proceedings of the International Conference on Machine Learning, 2022

Long Expressive Memory for Sequence Modeling.
Proceedings of the Tenth International Conference on Learning Representations, 2022


Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Higher-Order Quasi-Monte Carlo Training of Deep Neural Networks.
SIAM J. Sci. Comput., 2021

Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences.
SIAM J. Numer. Anal., 2021

On the approximation of functions by tanh neural networks.
Neural Networks, 2021

On Universal Approximation and Error Bounds for Fourier Neural Operators.
J. Mach. Learn. Res., 2021

A novel fourth-order WENO interpolation technique. A possible new tool designed for radiative transfer.
CoRR, 2021

Well-posedness of Bayesian inverse problems for hyperbolic conservation laws.
CoRR, 2021

On the well-posedness of Bayesian inversion for PDEs with ill-posed forward problems.
CoRR, 2021

Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs.
CoRR, 2021

Error estimates for DeepOnets: A deep learning framework in infinite dimensions.
CoRR, 2021

UnICORNN: A recurrent model for learning very long time dependencies.
Proceedings of the 38th International Conference on Machine Learning, 2021

Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Deep learning observables in computational fluid dynamics.
J. Comput. Phys., 2020

On the Convergence of the Spectral Viscosity Method for the Two-Dimensional Incompressible Euler Equations with Rough Initial Data.
Found. Comput. Math., 2020

Physics Informed Neural Networks for Simulating Radiative Transfer.
CoRR, 2020

Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks.
CoRR, 2020

Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs II: A class of inverse problems.
CoRR, 2020

Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs.
CoRR, 2020

On the conservation of energy in two-dimensional incompressible flows.
CoRR, 2020

2019
On the approximation of rough functions with deep neural networks.
CoRR, 2019

A Multi-level procedure for enhancing accuracy of machine learning algorithms.
CoRR, 2019

Statistical solutions of the incompressible Euler equations.
CoRR, 2019

Statistical solutions of hyperbolic systems of conservation laws: numerical approximation.
CoRR, 2019

Automatic Identification of Mixed Retinal Cells in Time-Lapse Fluorescent Microscopy Images using High-Dimensional DBSCAN.
Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019

2018
Numerical Approximation of Statistical Solutions of Scalar Conservation Laws.
SIAM J. Numer. Anal., 2018

A machine learning framework for data driven acceleration of computations of differential equations.
CoRR, 2018

2017
Construction of Approximate Entropy Measure-Valued Solutions for Hyperbolic Systems of Conservation Laws.
Found. Comput. Math., 2017

2016
On a Model for the Evolution of Morphogens in a Growing Tissue.
SIAM J. Math. Anal., 2016

Entropy stability and well-balancedness of space-time DG for the shallow water equations with bottom topography.
Networks Heterog. Media, 2016

Numerical Solution of Scalar Conservation Laws with Random Flux Functions.
SIAM/ASA J. Uncertain. Quantification, 2016

Multi-level Monte Carlo finite volume methods for uncertainty quantification of acoustic wave propagation in random heterogeneous layered medium.
J. Comput. Phys., 2016

On the computation of measure-valued solutions.
Acta Numer., 2016

2015
Schemes with Well-Controlled Dissipation.
SIAM J. Numer. Anal., 2015

2014
Entropy stable shock capturing space-time discontinuous Galerkin schemes for systems of conservation laws.
Numerische Mathematik, 2014

Numerical methods with controlled dissipation for small-scale dependent shocks.
Acta Numer., 2014

2013
Multi-level Monte Carlo Finite Volume Methods for Uncertainty Quantification in Nonlinear Systems of Balance Laws.
Proceedings of the Uncertainty Quantification in Computational Fluid Dynamics, 2013

Entropy Conservative and Entropy Stable Schemes for Nonconservative Hyperbolic Systems.
SIAM J. Numer. Anal., 2013

Convergence of vanishing capillarity approximations for scalar conservation laws with discontinuous fluxes.
Networks Heterog. Media, 2013

ENO Reconstruction and ENO Interpolation Are Stable.
Found. Comput. Math., 2013

2012
Multilevel Monte Carlo Finite Volume Methods for Shallow Water Equations with Uncertain Topography in Multi-dimensions.
SIAM J. Sci. Comput., 2012

Arbitrarily High-order Accurate Entropy Stable Essentially Nonoscillatory Schemes for Systems of Conservation Laws.
SIAM J. Numer. Anal., 2012

Sparse tensor multi-level Monte Carlo finite volume methods for hyperbolic conservation laws with random initial data.
Math. Comput., 2012

Entropy Stable Numerical Schemes for Two-Fluid Plasma Equations.
J. Sci. Comput., 2012

Multi-level Monte Carlo finite volume methods for nonlinear systems of conservation laws in multi-dimensions.
J. Comput. Phys., 2012

2011
Vorticity Preserving Finite Volume Schemes for the Shallow Water Equations.
SIAM J. Sci. Comput., 2011

Constraint Preserving Schemes Using Potential-Based Fluxes. II. Genuinely Multidimensional Systems of Conservation Laws.
SIAM J. Numer. Anal., 2011

Well-balanced and energy stable schemes for the shallow water equations with discontinuous topography.
J. Comput. Phys., 2011

Implicit-explicit schemes for flow equations with stiff source terms.
J. Comput. Appl. Math., 2011

Static Load Balancing for Multi-level Monte Carlo Finite Volume Solvers.
Proceedings of the Parallel Processing and Applied Mathematics, 2011

2010
Convergence of an Engquist-Osher scheme for a multi-dimensional triangular system of conservation laws.
Math. Comput., 2010

2009
Convergence of finite volume schemes for triangular systems of conservation laws.
Numerische Mathematik, 2009

Well-balanced schemes for conservation laws with source terms based on a local discontinuous flux formulation.
Math. Comput., 2009

Shock Capturing Artificial Dissipation for High-Order Finite Difference Schemes.
J. Sci. Comput., 2009

On the upstream mobility scheme for two-phase flow in porous media
CoRR, 2009

2007
Existence and stability of entropy solutions for a conservation law with discontinuous non-convex fluxes.
Networks Heterog. Media, 2007

Convergence of Godunov type methods for a conservation law with a spatially varying discontinuous flux function.
Math. Comput., 2007

2005
Convergence of Upwind Finite Difference Schemes for a Scalar Conservation Law with Indefinite Discontinuities in the Flux Function.
SIAM J. Numer. Anal., 2005


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