Multiscale modeling high-order methods and data-driven modeling
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Univ. of Michigan, Ann Arbor, MI (United States)
Projection-based reduced-order models (ROMs) comprise a promising set of data-driven approaches for accelerating the simulation of high-fidelity numerical simulations. Standard projection-based ROM approaches, however, suffer from several drawbacks when applied to the complex nonlinear dynamical systems commonly encountered in science and engineering. These limitations include a lack of stability, accuracy, and sharp a posteriori error estimators. This work addresses these limitations by leveraging multiscale modeling, least-squares principles, and machine learning to develop novel reduced-order modeling approaches, along with data-driven a posteriori error estimators, for dynamical systems. Theoretical and numerical results demonstrate that the two ROM approaches developed in this work - namely the windowed least-squares method and the Adjoint Petrov - Galerkin method - yield substantial improvements over state-of-the-art approaches. Additionally, numerical results demonstrate the capability of the a posteriori error models developed in this work.
- Research Organization:
- Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Univ. of Michigan, Ann Arbor, MI (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1673827
- Report Number(s):
- SAND--2020-11051R; 691422
- Country of Publication:
- United States
- Language:
- English
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