Multi-fidelity information fusion and resource allocation

Technical Report · Thu Sep 01 00:00:00 EDT 2022

DOI:https://doi.org/10.2172/1888363· OSTI ID:1888363

Jakeman, John D. ^[1]; Eldred, Michael S. ^[1]; Geraci, Gianluca ^[1]; Seidl, Daniel T. ^[1]; Smith, Thomas M. ^[1]; Gorodetsky, Alex A. ^[1]; Pham, Trung ^[1]; Narayan, Akil ^[1]; Zeng, Xiaoshu ^[1]; Ghanem, Roger ^[1]

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1. Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)

This project created and demonstrated a framework for the efficient and accurate prediction of complex systems with only a limited amount of highly trusted data. These next generation computational multi-fidelity tools fuse multiple information sources of varying cost and accuracy to reduce the computational and experimental resources needed for designing and assessing complex multi-physics/scale/component systems. These tools have already been used to substantially improve the computational efficiency of simulation aided modeling activities from assessing thermal battery performance to predicting material deformation. This report summarizes the work carried out during a two year LDRD project. Specifically we present our technical accomplishments; project outputs such as publications, presentations and professional leadership activities; and the project’s legacy.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: NA0003525
• OSTI ID: 1888363
• Report Number(s): SAND2022-12793; 710044; TRN: US2309333
• Country of Publication: United States
• Language: English

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