Projection-Based Dimensional Reduction of Adaptively Refined Nonlinear Models
Author:
Funder
Air Force Office of Scientific Research
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Applied Mathematics
Link
https://link.springer.com/content/pdf/10.1007/s42967-023-00308-3.pdf
Reference29 articles.
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2. Bank, R.E., Sherman, A.H.: An adaptive, multi-level method for elliptic boundary value problems. Computing 26(2), 91–105 (1981)
3. Barnett, J., Farhat, C.: Quadratic approximation manifold for mitigating the Kolmogorov barrier in nonlinear projection-based model order reduction. J. Comput. Phys. 464(111), 348 (2022). https://doi.org/10.1016/j.jcp.2022.111348
4. Borker, R., Huang, D., Grimberg, S., Farhat, C., Avery, P., Rabinovitch, J.B.R.: Mesh adaptation framework for embedded boundary methods for computational fluid dynamics and fluid-structure interaction. Int. J. Numer. Meth. Fluids 90(8), 389–424 (2019)
5. Carlberg, K., Bou-Mosleh, C., Farhat, C.: Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations. Int. J. Numer. Meth. Eng. 86(2), 155–181 (2011)
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