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pro vyhledávání: '"Callaham, Jared L."'
Many unsteady flows exhibiting complex dynamics are nevertheless characterized by emergent large-scale coherence in space and time. Reduced-order models based on Galerkin projection of the governing equations onto an orthogonal modal basis approximat
Externí odkaz:
http://arxiv.org/abs/2206.13205
Autor:
Kaptanoglu, Alan A., de Silva, Brian M., Fasel, Urban, Kaheman, Kadierdan, Goldschmidt, Andy J., Callaham, Jared L., Delahunt, Charles B., Nicolaou, Zachary G., Champion, Kathleen, Loiseau, Jean-Christophe, Kutz, J. Nathan, Brunton, Steven L.
Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySINDy is a Python package that provides tools for applying the sparse ide
Externí odkaz:
http://arxiv.org/abs/2111.08481
A major goal for reduced-order models of unsteady fluid flows is to uncover and exploit latent low-dimensional structure. Proper orthogonal decomposition (POD) provides an energy-optimal linear basis to represent the flow kinematics, but converges sl
Externí odkaz:
http://arxiv.org/abs/2106.02409
This work develops a low-dimensional nonlinear stochastic model of symmetry-breaking coherent structures from experimental measurements of a turbulent axisymmetric bluff body wake. Traditional model reduction methods decompose the field into a set of
Externí odkaz:
http://arxiv.org/abs/2105.13990
Autor:
Kaptanoglu, Alan A., Callaham, Jared L., Hansen, Christopher J., Aravkin, Aleksandr, Brunton, Steven L.
Publikováno v:
Phys. Rev. Fluids 6, 094401 (2021)
Modeling realistic fluid and plasma flows is computationally intensive, motivating the use of reduced-order models for a variety of scientific and engineering tasks. However, it is challenging to characterize, much less guarantee, the global stabilit
Externí odkaz:
http://arxiv.org/abs/2105.01843
Obtaining coarse-grained models that accurately incorporate finite-size effects is an important open challenge in the study of complex, multi-scale systems. We apply Langevin regression, a recently developed method for finding stochastic differential
Externí odkaz:
http://arxiv.org/abs/2103.16791
Publikováno v:
Proceedings of the Royal Society A 477, 2250 (2021)
Many physical systems characterized by nonlinear multiscale interactions can be effectively modeled by treating unresolved degrees of freedom as random fluctuations. However, even when the microscopic governing equations and qualitative macroscopic b
Externí odkaz:
http://arxiv.org/abs/2009.01006
Publikováno v:
Nature Communications 12, 1016 (2021)
Throughout the history of science, physics-based modeling has relied on judiciously approximating observed dynamics as a balance between a few dominant processes. However, this traditional approach is mathematically cumbersome and only applies in asy
Externí odkaz:
http://arxiv.org/abs/2001.10019
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