Fusion-DeepONet data
Year: 2025
Type: article
Abstract: This data has been used to generate all the results publihsed in our paper titled:"Fusion-DeepONet: A data-efficient neural operator for geometry-dependent hypersonic and supersonic flows."Published in the Journal of Computational Physics (2026). The DOI for the paper is: https://doi.org/10.1016/j.jcp.2025.114432
Source: Journal of Computational Physics
Author: Peyvan, Ahmad
Institution: Brown University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,980
OpenAlex ID: W7105647040
FWCI:
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