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Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty [Brochure]

Technical Report ·
DOI:https://doi.org/10.2172/2583338· OSTI ID:2583338
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [12];  [13];  [14];  [2];  [7];  [11];  [2];  [12];  [1] more »;  [15] « less
  1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. Johns Hopkins Univ., Baltimore, MD (United States)
  4. Dartmouth College, Hanover, NH (United States)
  5. Georgia Institute of Technology, Atlanta, GA (United States)
  6. Brookhaven National Laboratory (BNL), Upton, NY (United States)
  7. Argonne National Laboratory (ANL), Argonne, IL (United States)
  8. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  9. Univ. of Southern California, Los Angeles, CA (United States)
  10. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
  11. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
  12. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  13. Idaho National Laboratory (INL), Idaho Falls, ID (United States)
  14. Univ. of Washington, Seattle, WA (United States)
  15. Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
+ Show Author Affiliations

The four priority research directions outlined in this brochure represent a cohesive vision for advancing the science of inverse problems for complex systems under uncertainty. Together, they address the critical challenges of: discovering, exploiting, and preserving physical and problem structure; overcoming model limitations; integrating disparate, multimodal, and/or dynamic data; and tailoring the solution of inverse problems to downstream tasks. While each PRD focuses on a distinct aspect of inverse-problem research, their interconnected nature highlights the importance of a holistic approach that leverages progress across all areas to achieve transformative solutions. This agenda calls for research across mathematics, statistics, and computer science disciplines, which are guided and complemented by rapid advances in artificial intelligence, high-performance computing, and experimental facilities, to unlock new capabilities, maximize scientific impact, and meet the growing demands of inverse problems that arise across applications that are critical to DOE's mission.

Research Organization:
US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Advanced Scientific Computing Research (ASCR)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
OSTI ID:
2583338
Report Number(s):
DOESC20250610
Country of Publication:
United States
Language:
English

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