Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Carlberg, Kevin T."'
Autor:
Rodriguez, Steven N., Iliopoulos, Athanasios P., Carlberg, Kevin T., Brunton, Steven L., Steuben, John C., Michopoulos, John G.
This work presents a data-driven reduced-order modeling framework to accelerate the computations of $N$-body dynamical systems and their pair-wise interactions. The proposed framework differs from traditional acceleration methods, like the Barnes-Hut
Externí odkaz:
http://arxiv.org/abs/2103.01983
This work introduces Pressio, an open-source project aimed at enabling leading-edge projection-based reduced order models (ROMs) for large-scale nonlinear dynamical systems in science and engineering. Pressio provides model-reduction methods that can
Externí odkaz:
http://arxiv.org/abs/2003.07798
Autor:
Parish, Eric J., Carlberg, Kevin T.
This work proposes a windowed least-squares (WLS) approach for model-reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-model residual within a low-dimensional space-time trial subspace over ti
Externí odkaz:
http://arxiv.org/abs/1910.11388
Autor:
Parish, Eric J., Carlberg, Kevin T.
This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-learning error models (MLEM) framework proposed in Ref. 15 to dynamical
Externí odkaz:
http://arxiv.org/abs/1907.11822
Autor:
Etter, Philip A., Carlberg, Kevin T.
In many applications, projection-based reduced-order models (ROMs) have demonstrated the ability to provide rapid approximate solutions to high-fidelity full-order models (FOMs). However, there is no a priori assurance that these approximate solution
Externí odkaz:
http://arxiv.org/abs/1902.10659
Autor:
Carlberg, Kevin T., Jameson, Antony, Kochenderfer, Mykel J., Morton, Jeremy, Peng, Liqian, Witherden, Freddie D.
Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the number of times the solution is saved to disk, yet retain the ability to recover any field quantity (at any time instan
Externí odkaz:
http://arxiv.org/abs/1812.01177
This work introduces a new method to efficiently solve optimization problems constrained by partial differential equations (PDEs) with uncertain coefficients. The method leverages two sources of inexactness that trade accuracy for speed: (1) stochast
Externí odkaz:
http://arxiv.org/abs/1811.00177
Autor:
Freno, Brian A., Carlberg, Kevin T.
Publikováno v:
Computer Methods in Applied Mechanics and Engineering 348 (2019) 250--296
This work proposes a machine-learning framework for constructing statistical models of errors incurred by approximate solutions to parameterized systems of nonlinear equations. These approximate solutions may arise from early termination of an iterat
Externí odkaz:
http://arxiv.org/abs/1808.02097
Autor:
Rodriguez, Steven N., Iliopoulos, Athanasios P., Carlberg, Kevin T., Brunton, Steven L., Steuben, John C., Michopoulos, John G.
Publikováno v:
In Journal of Computational Physics 15 June 2022 459
Autor:
Parish, Eric J., Carlberg, Kevin T.
Publikováno v:
In Journal of Computational Physics 1 February 2021 426