Multifidelity data fusion in convolutional encoder/decoder networks
DOI10.1016/j.jcp.2022.111666OpenAlexW4298149695WikidataQ115041751 ScholiaQ115041751MaRDI QIDQ2099723
Lauren Partin, Gianluca Geraci, Michael S. Eldred, Daniele E. Schiavazzi, Ahmad A. Rushdi
Publication date: 18 November 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2205.05187
pressure Poisson equationuncertainty quantificationconvolutional encoder-deconder networksmultifidelity data fusion
Artificial intelligence (68Txx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Probabilistic methods, stochastic differential equations (65Cxx)
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Cites Work
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling
- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
- Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
- A generalized approximate control variate framework for multifidelity uncertainty quantification
- Multi-fidelity Bayesian neural networks: algorithms and applications
- A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- ON TRANSFER LEARNING OF NEURAL NETWORKS USING BI-FIDELITY DATA FOR UNCERTAINTY PROPAGATION
- An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems
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