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ORCIDLeonardo Zepeda-Núñez
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Journal Articles
- 2024
[j14]Borong Zhang
, Leonardo Zepeda-Núñez, Qin Li:
Solving the wide-band inverse scattering problem via equivariant neural networks. J. Comput. Appl. Math. 451: 116050 (2024)- 2023
- [j13]Yifan Peng, Lin Lin, Lexing Ying, Leonardo Zepeda-Núñez:
Efficient long-range convolutions for point clouds. J. Comput. Phys. 473: 111692 (2023) - [j12]Shi Chen, Zhiyan Ding, Qin Li, Leonardo Zepeda-Núñez:
High-Frequency Limit of the Inverse Scattering Problem: Asymptotic Convergence from Inverse Helmholtz to Inverse Liouville. SIAM J. Imaging Sci. 16(1): 111-143 (2023) - [j11]Gideon Dresdner, Dmitrii Kochkov, Peter Christian Norgaard, Leonardo Zepeda-Núñez, Jamie A. Smith, Michael P. Brenner, Stephan Hoyer:
Learning to correct spectral methods for simulating turbulent flows. Trans. Mach. Learn. Res. 2023 (2023) - 2022
- [j10]Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez:
Wide-Band Butterfly Network: Stable and Efficient Inversion Via Multi-Frequency Neural Networks. Multiscale Model. Simul. 20(4): 1191-1227 (2022) - [j9]Tan Bui-Thanh, Qin Li, Leonardo Zepeda-Núñez:
Bridging and Improving Theoretical and Computational Electrical Impedance Tomography via Data Completion. SIAM J. Sci. Comput. 44(3): 668- (2022) - 2021
- [j8]Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang, Weile Jia, Linfeng Zhang, Lin Lin:
Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks. J. Comput. Phys. 443: 110523 (2021) - 2020
- [j7]Matthias Taus, Leonardo Zepeda-Núñez, Russell J. Hewett, Laurent Demanet:
L-Sweeps: A scalable, parallel preconditioner for the high-frequency Helmholtz equation. J. Comput. Phys. 420: 109706 (2020) - 2019
- [j6]Yuwei Fan, Lin Lin, Lexing Ying, Leonardo Zepeda-Núñez:
A Multiscale Neural Network Based on Hierarchical Matrices. Multiscale Model. Simul. 17(4): 1189-1213 (2019) - [j5]Lin Lin, Leonardo Zepeda-Núñez:
Projection-Based Embedding Theory for Solving Kohn-Sham Density Functional Theory. Multiscale Model. Simul. 17(4): 1274-1300 (2019) - 2018
- [j4]Jun Fang, Jianliang Qian, Leonardo Zepeda-Núñez, Hongkai Zhao:
A hybrid approach to solve the high-frequency Helmholtz equation with source singularity in smooth heterogeneous media. J. Comput. Phys. 371: 261-279 (2018) - [j3]Leonardo Zepeda-Núñez, Laurent Demanet:
Nested Domain Decomposition with Polarized Traces for the 2D Helmholtz Equation. SIAM J. Sci. Comput. 40(3) (2018) - 2016
- [j2]Leonardo Zepeda-Núñez, Laurent Demanet:
The method of polarized traces for the 2D Helmholtz equation. J. Comput. Phys. 308: 347-388 (2016) - [j1]Leonardo Zepeda-Núñez, Hongkai Zhao:
Fast Alternating BiDirectional Preconditioner for the 2D High-Frequency Lippmann-Schwinger Equation. SIAM J. Sci. Comput. 38(5) (2016)
Conference and Workshop Papers
- 2024
- [c5]Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez:
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. ICML 2024 - 2023
- [c4]Zhong Yi Wan, Leonardo Zepeda-Núñez, Anudhyan Boral, Fei Sha:
Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems. ICLR 2023 - [c3]Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez:
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. ICML 2023: 10136-10152 - [c2]Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Núñez, James Lottes, Qing Wang, Yi-Fan Chen, John Anderson, Fei Sha:
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations. NeurIPS 2023 - [c1]Zhong Yi Wan, Ricardo Baptista, Anudhyan Boral, Yi-Fan Chen, John Anderson, Fei Sha, Leonardo Zepeda-Núñez:
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models. NeurIPS 2023
Informal and Other Publications
- 2024
- [i20]Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez:
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. CoRR abs/2402.04467 (2024) - [i19]Benedikt Barthel Sorensen, Leonardo Zepeda-Núñez, Ignacio Lopez-Gomez, Zhong Yi Wan, Rob Carver, Fei Sha, Themistoklis P. Sapsis:
A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data. CoRR abs/2408.02688 (2024) - [i18]Borong Zhang, Martin Guerra, Qin Li, Leonardo Zepeda-Núñez:
Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models. CoRR abs/2408.02866 (2024) - [i17]Shantanu Shahane, Sheide Chammas, Deniz A. Bezgin, Aaron B. Buhendwa, Steffen J. Schmidt, Nikolaus A. Adams, Spencer H. Bryngelson, Yi-Fan Chen, Qing Wang, Fei Sha, Leonardo Zepeda-Núñez:
Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme. CoRR abs/2409.09217 (2024) - [i16]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) - [i15]Ignacio Lopez-Gomez, Zhong Yi Wan, Leonardo Zepeda-Núñez, Tapio Schneider, John R. Anderson, Fei Sha:
Dynamical-generative downscaling of climate model ensembles. CoRR abs/2410.01776 (2024) - 2023
- [i14]Zhong Yi Wan, Leonardo Zepeda-Núñez, Anudhyan Boral, Fei Sha:
Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems. CoRR abs/2301.10391 (2023) - [i13]Zhong Yi Wan, Ricardo Baptista, Yi-Fan Chen, John Anderson, Anudhyan Boral, Fei Sha, Leonardo Zepeda-Núñez:
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models. CoRR abs/2305.15618 (2023) - [i12]Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Núñez, James Lottes, Qing Wang, Yi-Fan Chen, John Robert Anderson, Fei Sha:
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations. CoRR abs/2306.01174 (2023) - [i11]Marc Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez:
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. CoRR abs/2306.07526 (2023) - 2022
- [i10]Shi Chen, Zhiyan Ding, Qin Li, Leonardo Zepeda-Núñez:
High-frequency limit of the inverse scattering problem: asymptotic convergence from inverse Helmholtz to inverse Liouville. CoRR abs/2201.03494 (2022) - [i9]Gideon Dresdner, Dmitrii Kochkov, Peter C. Norgaard, Leonardo Zepeda-Núñez, Jamie A. Smith, Michael P. Brenner, Stephan Hoyer:
Learning to correct spectral methods for simulating turbulent flows. CoRR abs/2207.00556 (2022) - [i8]Borong Zhang, Leonardo Zepeda-Núñez, Qin Li:
Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks. CoRR abs/2212.06068 (2022) - 2021
- [i7]Tan Bui-Thanh, Qin Li, Leonardo Zepeda-Núñez:
Bridging and Improving Theoretical and Computational Electric Impedance Tomography via Data Completion. CoRR abs/2105.00554 (2021) - [i6]Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez:
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime. CoRR abs/2106.01143 (2021) - 2020
- [i5]Jiefu Zhang, Leonardo Zepeda-Núñez, Yuan Yao, Lin Lin:
Learning the mapping $\mathbf{x}\mapsto \sum_{i=1}^d x_i^2$: the cost of finding the needle in a haystack. CoRR abs/2002.10561 (2020) - [i4]Yifan Peng, Lin Lin, Lexing Ying, Leonardo Zepeda-Núñez:
Efficient Long-Range Convolutions for Point Clouds. CoRR abs/2010.05295 (2020) - [i3]Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez:
Wide-band butterfly network: stable and efficient inversion via multi-frequency neural networks. CoRR abs/2011.12413 (2020) - 2019
- [i2]Matthias Taus, Leonardo Zepeda-Núñez, Russell J. Hewett, Laurent Demanet:
L-Sweeps: A scalable, parallel preconditioner for the high-frequency Helmholtz equation. CoRR abs/1909.01467 (2019) - [i1]Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang, Weile Jia, Linfeng Zhang, Lin Lin:
Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks. CoRR abs/1912.00775 (2019)
Coauthor Index
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