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06/13/2023
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems
Diffusion models are a class of probabilistic generative models that hav...
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05/24/2023
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
We introduce a two-stage probabilistic framework for statistical downsca...
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01/25/2023
Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems
We present a data-driven, space-time continuous framework to learn surro...
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12/12/2022
Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
This paper introduces a novel deep neural network architecture for solvi...
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07/01/2022
Learning to correct spectral methods for simulating turbulent flows
Despite their ubiquity throughout science and engineering, only a handfu...
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01/10/2022
High-frequency limit of the inverse scattering problem: asymptotic convergence from inverse Helmholtz to inverse Liouville
We investigate the asymptotic relation between the inverse problems rely...
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06/02/2021
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime
We propose an end-to-end deep learning framework that comprehensively so...
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05/02/2021
Bridging and Improving Theoretical and Computational Electric Impedance Tomography via Data Completion
In computational PDE-based inverse problems, a finite amount of data is ...
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11/24/2020
Wide-band butterfly network: stable and efficient inversion via multi-frequency neural networks
We introduce an end-to-end deep learning architecture called the wide-ba...
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10/11/2020
Efficient Long-Range Convolutions for Point Clouds
The efficient treatment of long-range interactions for point clouds is a...
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02/24/2020
Learning the mapping x∑_i=1^d x_i^2: the cost of finding the needle in a haystack
The task of using machine learning to approximate the mapping x∑_i=1^d x...
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11/27/2019
Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks
The recently developed Deep Potential [Phys. Rev. Lett. 120, 143001, 201...
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09/03/2019