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Omar Ghattas

Efficient geometric Markov chain Monte Carlo for nonlinear Bayesian inversion enabled by derivative-informed neural operators

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Mar 13, 2024
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Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators

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May 31, 2023
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Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems

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Oct 06, 2022
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Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning

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Jun 23, 2022
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Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport

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Jun 22, 2022
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A stochastic Stein Variational Newton method

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Apr 19, 2022
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Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data

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Dec 14, 2021
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Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs

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Nov 30, 2020
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Projected Stein Variational Gradient Descent

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Feb 09, 2020
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Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training

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Feb 07, 2020
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