Discovering new governing equations using ML
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Univ. of Utah, Salt Lake City, UT (United States)
- Univ. of Washington, Seattle, WA (United States)
- Univ. of Illinois at Urbana-Champaign, IL (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
A hallmark of the scientific process since the time of Newton has been the derivation of mathematical equations meant to capture relationships between observables. As the field of mathematical modeling evolved, practitioners specifically emphasized mathematical formulations that were predictive, generalizable, and interpretable. Machine learning’s ability to interrogate complex processes is particularly useful for the analysis of highly heterogeneous, anisotropic materials where idealized descriptions often fail. As we move into this new era, we anticipate the need to leverage machine learning to aid scientists in extracting meaningful, but yet sometimes elusive, relationships between observed quantities.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- NA0003525
- OSTI ID:
- 1832308
- Report Number(s):
- SAND2021-14093R; 701900
- Country of Publication:
- United States
- Language:
- English
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