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05/31/2023
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators
We propose a novel machine learning framework for solving optimization p...
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10/06/2022
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems
We explore using neural operators, or neural network representations of ...
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06/21/2022
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Neural operators have gained significant attention recently due to their...
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01/20/2022
Derivative-informed projected neural network for large-scale Bayesian optimal experimental design
We address the solution of large-scale Bayesian optimal experimental des...
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12/14/2021
Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data
We present a parsimonious surrogate framework for learning high dimensio...
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11/30/2020
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs
Many-query problems, arising from uncertainty quantification, Bayesian i...
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02/07/2020
Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training
In this work we analyze the role nonlinear activation functions play at ...
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02/07/2020
