Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models-Reference-Cited by-同舟云学术

Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models

Published:2023-01 Issue:1 Volume:51 Page:109-119
ISSN:0093-3813
Container-title:IEEE Transactions on Plasma Science
language:
Short-container-title:IEEE Trans. Plasma Sci.

Author:

Vander Wal Michael D.¹^ORCID,McClarren Ryan G.¹^ORCID,Humbird Kelli D.²

Affiliation:

1. Aerospace and Mechanical Engineering Department, University of Notre Dame, Notre Dame, IN, USA

2. Lawrence Livermore National Laboratory, Livermore, CA, USA

Funder

U.S. Department of Energy

Lawrence Livermore National Laboratory

Academic Collaboration Team University Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Condensed Matter Physics,Nuclear and High Energy Physics

Link

http://xplorestaging.ieee.org/ielx7/27/10024523/10015650.pdf?arnumber=10015650

Reference43 articles.

1. Improving the generative performance of chemical autoencoders through transfer learning

2. Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration

3. Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data

4. Transfer learning enhanced physics informed neural network for phase-field modeling of fracture

5. Transfer learning as a tool for reducing simulation bias: Application to inertial confinement fusion;kustowski;Proc 60th Annu Meeting APS Division Plasma Phys,2019

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