default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDSiddhartha Mishra
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j41]Tim De Ryck
, Siddhartha Mishra:
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning. Acta Numer. 33: 633-713 (2024)- [j40]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): 5099-5143 (2024) - [j39]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): 811-841 (2024) - [j38]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) - [c19]Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac:
An operator preconditioning perspective on training in physics-informed machine learning. ICLR 2024 - [c18]Levi E. Lingsch, Mike Yan Michelis, Emmanuel de Bézenac, Sirani M. Perera, Robert K. Katzschmann, Siddhartha Mishra:
Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains. ICML 2024 - [c17]Tobias Rohner, Siddhartha Mishra:
Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows. PASC 2024: 8:1-8:11 - [i54]Tim De Ryck, Siddhartha Mishra:
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning. CoRR abs/2402.10926 (2024) - [i53]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 abs/2403.07384 (2024) - [i52]Levi E. Lingsch, Dana Grund, Siddhartha Mishra, Georgios Kissas:
FUSE: Fast Unified Simulation and Estimation for PDEs. CoRR abs/2405.14558 (2024) - [i51]Maximilian Herde, Bogdan Raonic, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra:
Poseidon: Efficient Foundation Models for PDEs. CoRR abs/2405.19101 (2024) - [i50]Roberto Molinaro, Samuel Lanthaler, Bogdan Raonic, Tobias Rohner, Victor Armegioiu, Zhong Yi Wan, Fei Sha, Siddhartha Mishra, Leonardo Zepeda-Núñez:
Generative AI for fast and accurate Statistical Computation of Fluids. CoRR abs/2409.18359 (2024) - 2023
- [j37]Marco Petrella, Rémi Abgrall, Siddhartha Mishra:
On the discrete equation model for compressible multiphase fluid flows. J. Comput. Phys. 478: 111974 (2023) - [c16]Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra:
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. ICLR 2023 - [c15]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 - [c14]Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra:
Neural Inverse Operators for Solving PDE Inverse Problems. ICML 2023: 25105-25139 - [c13]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 - [c12]Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra:
Neural Oscillators are Universal. NeurIPS 2023 - [c11]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 - [i49]Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra:
Neural Inverse Operators for Solving PDE Inverse Problems. CoRR abs/2301.11167 (2023) - [i48]Bogdan Raonic, Roberto Molinaro, Tobias Rohner, Siddhartha Mishra, Emmanuel de Bézenac:
Convolutional Neural Operators. CoRR abs/2302.01178 (2023) - [i47]Léonard Equer, T. Konstantin Rusch, Siddhartha Mishra:
Multi-Scale Message Passing Neural PDE Solvers. CoRR abs/2302.03580 (2023) - [i46]T. Konstantin Rusch, Michael M. Bronstein, Siddhartha Mishra:
A Survey on Oversmoothing in Graph Neural Networks. CoRR abs/2303.10993 (2023) - [i45]Marco Petrella, Rémi Abgrall, Siddhartha Mishra:
A Monte-Carlo ab-initio algorithm for the multiscale simulation of compressible multiphase flows. CoRR abs/2303.16540 (2023) - [i44]Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra:
Neural Oscillators are Universal. CoRR abs/2305.08753 (2023) - [i43]Levi E. Lingsch, Mike Yan Michelis, Sirani M. Perera, Robert K. Katzschmann, Siddhartha Mishra:
Vandermonde Neural Operators. CoRR abs/2305.19663 (2023) - [i42]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 abs/2305.19913 (2023) - [i41]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 abs/2306.03589 (2023) - [i40]Victorita Dolean, Alexander Heinlein, Siddhartha Mishra, Ben Moseley:
Multilevel domain decomposition-based architectures for physics-informed neural networks. CoRR abs/2306.05486 (2023) - [i39]Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac:
An operator preconditioning perspective on training in physics-informed machine learning. CoRR abs/2310.05801 (2023) - [i38]Benjamin Scellier, Siddhartha Mishra:
A universal approximation theorem for nonlinear resistive networks. CoRR abs/2312.15063 (2023) - 2022
- [j36]Tim De Ryck, Siddhartha Mishra:
Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs. Adv. Comput. Math. 48(6): 79 (2022) - [c10]Siddhartha Mishra, Nicholas Monath, Michael Boratko, Ariel Kobren, Andrew McCallum:
An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity. AAAI 2022: 7788-7796 - [c9]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: 2263-2276 - [c8]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: 5085-5109 - [c7]T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney:
Long Expressive Memory for Sequence Modeling. ICLR 2022 - [c6]T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein:
Graph-Coupled Oscillator Networks. ICML 2022: 18888-18909 - [c5]Tim De Ryck, Siddhartha Mishra:
Generic bounds on the approximation error for physics-informed (and) operator learning. NeurIPS 2022 - [i37]T. Konstantin Rusch, Benjamin Paul Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein:
Graph-Coupled Oscillator Networks. CoRR abs/2202.02296 (2022) - [i36]Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra:
Error estimates for physics informed neural networks approximating the Navier-Stokes equations. CoRR abs/2203.09346 (2022) - [i35]Marco Petrella, Rémi Abgrall, Siddhartha Mishra:
On the discrete equation model for compressible multiphase fluid flows. CoRR abs/2204.01083 (2022) - [i34]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 A, 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 abs/2204.07705 (2022) - [i33]Tim De Ryck, Siddhartha Mishra:
Generic bounds on the approximation error for physics-informed (and) operator learning. CoRR abs/2205.11393 (2022) - [i32]Michael Prasthofer, Tim De Ryck, Siddhartha Mishra:
Variable-Input Deep Operator Networks. CoRR abs/2205.11404 (2022) - [i31]Benjamin Scellier, Siddhartha Mishra, Yoshua Bengio, Yann Ollivier:
Agnostic Physics-Driven Deep Learning. CoRR abs/2205.15021 (2022) - [i30]Tim De Ryck, Siddhartha Mishra:
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws. CoRR abs/2207.07362 (2022) - [i29]Tim De Ryck, Siddhartha Mishra, Roberto Molinaro:
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws. CoRR abs/2207.08483 (2022) - [i28]T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra:
Gradient Gating for Deep Multi-Rate Learning on Graphs. CoRR abs/2210.00513 (2022) - [i27]Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra:
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. CoRR abs/2210.01074 (2022) - [i26]Victorita Dolean, Alexander Heinlein, Siddhartha Mishra, Ben Moseley:
Finite basis physics-informed neural networks as a Schwarz domain decomposition method. CoRR abs/2211.05560 (2022) - 2021
- [j35]Nikola B. Kovachki, Samuel Lanthaler, Siddhartha Mishra:
On Universal Approximation and Error Bounds for Fourier Neural Operators. J. Mach. Learn. Res. 22: 290:1-290:76 (2021) - [j34]Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra:
On the approximation of functions by tanh neural networks. Neural Networks 143: 732-750 (2021) - [j33]Siddhartha Mishra, T. Konstantin Rusch:
Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences. SIAM J. Numer. Anal. 59(3): 1811-1834 (2021) - [j32]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): A3938-A3966 (2021) - [c4]T. Konstantin Rusch, Siddhartha Mishra:
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies. ICLR 2021 - [c3]T. Konstantin Rusch, Siddhartha Mishra:
UnICORNN: A recurrent model for learning very long time dependencies. ICML 2021: 9168-9178 - [i25]Samuel Lanthaler, Siddhartha Mishra, George Em Karniadakis:
Error estimates for DeepOnets: A deep learning framework in infinite dimensions. CoRR abs/2102.09618 (2021) - [i24]T. Konstantin Rusch, Siddhartha Mishra:
UnICORNN: A recurrent model for learning very long time dependencies. CoRR abs/2103.05487 (2021) - [i23]Genming Bai, Ujjwal Koley, Siddhartha Mishra, Roberto Molinaro:
Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs. CoRR abs/2104.05584 (2021) - [i22]Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra:
On the approximation of functions by tanh neural networks. CoRR abs/2104.08938 (2021) - [i21]Tim De Ryck, Siddhartha Mishra:
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs. CoRR abs/2106.14473 (2021) - [i20]Nikola B. Kovachki, Samuel Lanthaler, Siddhartha Mishra:
On universal approximation and error bounds for Fourier Neural Operators. CoRR abs/2107.07562 (2021) - [i19]Samuel Lanthaler, Siddhartha Mishra, Franziska Weber:
On the well-posedness of Bayesian inversion for PDEs with ill-posed forward problems. CoRR abs/2107.07593 (2021) - [i18]Siddhartha Mishra, David Ochsner, Adrian Montgomery Ruf, Franziska Weber:
Well-posedness of Bayesian inverse problems for hyperbolic conservation laws. CoRR abs/2107.09701 (2021) - [i17]T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney:
Long Expressive Memory for Sequence Modeling. CoRR abs/2110.04744 (2021) - [i16]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 abs/2110.11885 (2021) - 2020
- [j31]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): 1309-1362 (2020) - [j30]Kjetil O. Lye, Siddhartha Mishra, Deep Ray:
Deep learning observables in computational fluid dynamics. J. Comput. Phys. 410: 109339 (2020) - [i15]Samuel Lanthaler, Siddhartha Mishra, Carlos Parés-Pulido:
On the conservation of energy in two-dimensional incompressible flows. CoRR abs/2001.06195 (2020) - [i14]Siddhartha Mishra, T. Konstantin Rusch:
Enhancing accuracy of deep learning algorithms by training with low-discrepancy sequences. CoRR abs/2005.12564 (2020) - [i13]Siddhartha Mishra, Roberto Molinaro:
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs. CoRR abs/2006.16144 (2020) - [i12]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 abs/2007.01138 (2020) - [i11]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 abs/2008.05730 (2020) - [i10]Marcello Longo, Siddhartha Mishra, T. Konstantin Rusch, Christoph Schwab:
Higher-order Quasi-Monte Carlo Training of Deep Neural Networks. CoRR abs/2009.02713 (2020) - [i9]Siddhartha Mishra, Roberto Molinaro:
Physics Informed Neural Networks for Simulating Radiative Transfer. CoRR abs/2009.13291 (2020) - [i8]T. Konstantin Rusch, Siddhartha Mishra:
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies. CoRR abs/2010.00951 (2020)
2010 – 2019
- 2019
- [c2]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: 4783-4786 - [i7]Kjetil O. Lye, Siddhartha Mishra, Deep Ray:
Deep learning observables in computational fluid dynamics. CoRR abs/1903.03040 (2019) - [i6]Ulrik Skre Fjordholm, Kjetil O. Lye, Siddhartha Mishra, Franziska Weber:
Statistical solutions of hyperbolic systems of conservation laws: numerical approximation. CoRR abs/1906.02536 (2019) - [i5]Samuel Lanthaler, Siddhartha Mishra, Carlos Parés-Pulido:
Statistical solutions of the incompressible Euler equations. CoRR abs/1909.06615 (2019) - [i4]Kjetil O. Lye, Siddhartha Mishra, Roberto Molinaro:
A Multi-level procedure for enhancing accuracy of machine learning algorithms. CoRR abs/1909.09448 (2019) - [i3]Tim De Ryck, Siddhartha Mishra, Deep Ray:
On the approximation of rough functions with deep neural networks. CoRR abs/1912.06732 (2019) - 2018
- [j29]Ulrik S. Fjordholm, Kjetil O. Lye, Siddhartha Mishra:
Numerical Approximation of Statistical Solutions of Scalar Conservation Laws. SIAM J. Numer. Anal. 56(5): 2989-3009 (2018) - [i2]Siddhartha Mishra:
A machine learning framework for data driven acceleration of computations of differential equations. CoRR abs/1807.09519 (2018) - 2017
- [j28]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): 763-827 (2017) - 2016
- [j27]Ulrik S. Fjordholm, Siddhartha Mishra, Eitan Tadmor:
On the computation of measure-valued solutions. Acta Numer. 25: 567-679 (2016) - [j26]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: 192-217 (2016) - [j25]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): 552-591 (2016) - [j24]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): 145-162 (2016) - [j23]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): 1575-1615 (2016) - 2015
- [j22]Jan Ernest, Philippe G. LeFloch, Siddhartha Mishra:
Schemes with Well-Controlled Dissipation. SIAM J. Numer. Anal. 53(1): 674-699 (2015) - 2014
- [j21]Philippe G. LeFloch, Siddhartha Mishra:
Numerical methods with controlled dissipation for small-scale dependent shocks. Acta Numer. 23: 743-816 (2014) - [j20]Andreas Hiltebrand, Siddhartha Mishra:
Entropy stable shock capturing space-time discontinuous Galerkin schemes for systems of conservation laws. Numerische Mathematik 126(1): 103-151 (2014) - 2013
- [j19]Ulrik S. Fjordholm, Siddhartha Mishra, Eitan Tadmor:
ENO Reconstruction and ENO Interpolation Are Stable. Found. Comput. Math. 13(2): 139-159 (2013) - [j18]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): 969-984 (2013) - [j17]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): 1371-1391 (2013) - [p1]Siddhartha Mishra, Christoph Schwab, Jonas Sukys:
Multi-level Monte Carlo Finite Volume Methods for Uncertainty Quantification in Nonlinear Systems of Balance Laws. Uncertainty Quantification in Computational Fluid Dynamics 2013: 225-294 - [e1]Hester Bijl, Didier Lucor, Siddhartha Mishra, Christoph Schwab:
Uncertainty Quantification in Computational Fluid Dynamics. Lecture Notes in Computational Science and Engineering 92, Springer 2013, ISBN 978-3-319-00884-4 [contents] - 2012
- [j16]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): 3365-3388 (2012) - [j15]Harish Kumar, Siddhartha Mishra:
Entropy Stable Numerical Schemes for Two-Fluid Plasma Equations. J. Sci. Comput. 52(2): 401-425 (2012) - [j14]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): 1979-2018 (2012) - [j13]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): 544-573 (2012) - [j12]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) - 2011
- [j11]Magnus Svärd, Siddhartha Mishra:
Implicit-explicit schemes for flow equations with stiff source terms. J. Comput. Appl. Math. 235(6): 1564-1577 (2011) - [j10]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): 5587-5609 (2011) - [j9]Siddhartha Mishra, Eitan Tadmor:
Constraint Preserving Schemes Using Potential-Based Fluxes. II. Genuinely Multidimensional Systems of Conservation Laws. SIAM J. Numer. Anal. 49(3): 1023-1045 (2011) - [j8]Ulrik S. Fjordholm, Siddhartha Mishra:
Vorticity Preserving Finite Volume Schemes for the Shallow Water Equations. SIAM J. Sci. Comput. 33(2): 588-611 (2011) - [c1]Jonas Sukys, Siddhartha Mishra, Christoph Schwab:
Static Load Balancing for Multi-level Monte Carlo Finite Volume Solvers. PPAM (1) 2011: 245-254 - 2010
- [j7]Giuseppe Maria Coclite, Siddhartha Mishra, Nils Henrik Risebro:
Convergence of an Engquist-Osher scheme for a multi-dimensional triangular system of conservation laws. Math. Comput. 79(269): 71-94 (2010)
2000 – 2009
- 2009
- [j6]Magnus Svärd, Siddhartha Mishra:
Shock Capturing Artificial Dissipation for High-Order Finite Difference Schemes. J. Sci. Comput. 39(3): 454-484 (2009) - [j5]Kenneth H. Karlsen, Siddhartha Mishra, Nils Henrik Risebro:
Well-balanced schemes for conservation laws with source terms based on a local discontinuous flux formulation. Math. Comput. 78(265): 55-78 (2009) - [j4]Kenneth H. Karlsen, Siddhartha Mishra, Nils Henrik Risebro:
Convergence of finite volume schemes for triangular systems of conservation laws. Numerische Mathematik 111(4): 559-589 (2009) - [i1]Siddhartha Mishra, Jérôme Jaffré:
On the upstream mobility scheme for two-phase flow in porous media. CoRR abs/0901.4032 (2009) - 2007
- [j3]Adimurthi, Siddhartha Mishra, G. D. Veerappa Gowda:
Convergence of Godunov type methods for a conservation law with a spatially varying discontinuous flux function. Math. Comput. 76(259): 1219-1242 (2007) - [j2]Adimurthi, Siddhartha Mishra, G. D. Veerappa Gowda:
Existence and stability of entropy solutions for a conservation law with discontinuous non-convex fluxes. Networks Heterog. Media 2(1): 127-157 (2007) - 2005
- [j1]Siddhartha Mishra:
Convergence of Upwind Finite Difference Schemes for a Scalar Conservation Law with Indefinite Discontinuities in the Flux Function. SIAM J. Numer. Anal. 43(2): 559-577 (2005)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-18 19:30 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
