AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems
- Univ. of Texas, Austin, TX (United States)
The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for (1) learning predictive models from data and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addressed the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models---the so-called outer loop. This report summarizes the work done under DE-SC0021077 on (1) nonlocal models for solidification problems, (2) a multifidelity method for a nonlocal diffusion model, and (3) multifidelity Monte Carlo methods.
- Research Organization:
- Univ. of Texas, Austin, TX (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- DOE Contract Number:
- SC0021077
- OSTI ID:
- 2482273
- Report Number(s):
- DOE-UT--SC0021077
- Country of Publication:
- United States
- Language:
- English
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