Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jared L. Callaham"'
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
The dynamics of complex physical systems can be determined by the balance of a few dominant processes. Callaham et al. propose a machine learning approach for the identification of dominant regimes from experimental or numerical data with examples fr
Externí odkaz:
https://doaj.org/article/d464131237b648b2a94206b7d3ce44f4
Autor:
Brian M. de Silva, Steven L. Brunton, Jonathan Jonker, Nathan Hicks, Darren McDonald, Jennifer Klemisch, Nicholas Goebel, J. Nathan Kutz, Jared L. Callaham, Aleksandr Y. Aravkin
Publikováno v:
AIAA Journal. 59:3490-3503
Data-driven algorithms are developed to fully automate sensor fault detection in systems governed by underlying physics, with a particular focus on the flight test setting. The proposed machine lea...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::094b0c85b38cca92979c9a6a98a4e241
https://doi.org/10.2514/1.j059943
https://doi.org/10.2514/1.j059943
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc4b969b545337811bb0fbe554f00313
http://hdl.handle.net/10044/1/97244
http://hdl.handle.net/10044/1/97244
Publikováno v:
Journal of Fluid Mechanics. 938
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7e6f6fd33f2f0f7a2d5395c9fdcebd0
https://doi.org/10.1017/jfm.2021.994
https://doi.org/10.1017/jfm.2021.994
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::728ff795d5bb9047128f481479e2feba
http://arxiv.org/abs/2103.16791
http://arxiv.org/abs/2103.16791
Publikováno v:
Nature Communications
Nature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
Nature Communications, Vol 12, Iss 1, Pp 1-10 (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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a4ce3fc66b852dd2fe12e13a967d59e
http://europepmc.org/articles/PMC7884409
http://europepmc.org/articles/PMC7884409
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af5519de41b49278eaabf12fcbb79d84
http://arxiv.org/abs/2009.01006
http://arxiv.org/abs/2009.01006
Autor:
Jared L. Callaham, Jonathan Machta
Publikováno v:
Physical review. E. 95(6-1)
Population annealing is a sequential Monte Carlo scheme well-suited to simulating equilibrium states of systems with rough free energy landscapes. Here we use population annealing to study a binary mixture of hard spheres. Population annealing is a p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bdd39296aeb378dd011df71b098f967
https://pubmed.ncbi.nlm.nih.gov/28709207
https://pubmed.ncbi.nlm.nih.gov/28709207