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03/20/2023
Bi-orthogonal fPINN: A physics-informed neural network method for solving time-dependent stochastic fractional PDEs
Fractional partial differential equations (FPDEs) can effectively repres...
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02/07/2023
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning
In this paper, a novel framework is established for uncertainty quantifi...
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03/16/2022
Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations
We introduce a sampling based machine learning approach, Monte Carlo phy...
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08/30/2021
Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models
We introduce in this work the normalizing field flows (NFF) for learning...
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08/23/2020
Sparse approximation of data-driven Polynomial Chaos expansions: an induced sampling approach
One of the open problems in the field of forward uncertainty quantificat...
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12/10/2019
Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks
Deep neural networks (DNNs) perform well on a variety of tasks despite t...
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12/02/2019
Speaker detection in the wild: Lessons learned from JSALT 2019
This paper presents the problems and solutions addressed at the JSALT wo...
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05/03/2019
Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks
One of the open problems in scientific computing is the long-time integr...
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04/16/2019
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST s...
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09/21/2018
