Zobrazeno 1 - 10
of 17
pro vyhledávání: '"David Sondak"'
Publikováno v:
The Astrophysical Journal, Vol 964, Iss 1, p 2 (2024)
Advancing our understanding of astrophysical turbulence is bottlenecked by the limited resolution of numerical simulations that may not fully sample scales in the inertial range. Machine-learning (ML) techniques have demonstrated promise in upscaling
Externí odkaz:
https://doaj.org/article/9daa6f0deed84a6fb8c8778da6087aa6
Publikováno v:
Journal of Open Source Education. 5:161
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3368416228a6dd24479d81121a4368cd
https://doi.org/10.21105/jose.00161
https://doi.org/10.21105/jose.00161
Accurately learning the temporal behavior of dynamical systems requires models with well-chosen learning biases. Recent innovations embed the Hamiltonian and Lagrangian formalisms into neural networks and demonstrate a significant improvement over ot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2395db8da76b6ed8c58a2667e4a72e43
Autor:
David Sondak, Pavlos Protopapas
Machine learning models have emerged as powerful tools in physics and engineering. Although flexible, a fundamental challenge remains on how to connect new machine learning models with known physics. In this work, we present an autoencoder with laten
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51ca507bb4a41614b2c073471a62656a
http://arxiv.org/abs/2011.07346
http://arxiv.org/abs/2011.07346
For two-dimensional Rayleigh-B\'{e}nard convection, classes of unstable, steady solutions were previously computed using numerical continuation (Waleffe, 2015; Sondak, 2015). The `primary' steady solution bifurcates from the conduction state at $Ra \
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49fb073626d52265a209739e92fb9ca7
http://arxiv.org/abs/2006.14132
http://arxiv.org/abs/2006.14132
Autor:
David Sondak, Feiyu Chen, Shuheng Liu, Devansh Agarwal, Pavlos Protopapas, Marios Mattheakis, Marco Di Giovanni
NeuroDiffEq is a Python package built with PyTorch that uses ANNs to solve ordinary and partial differential equations (ODEs and PDEs). During the release of NeuroDiffEq we discovered that two other groups had almost simultaneously released their own
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a152570f8eef59e8eb0c4acb91c817a
There has been a wave of interest in applying machine learning to study dynamical systems. We present a Hamiltonian neural network that solves the differential equations that govern dynamical systems. This is an equation-driven machine learning metho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed1492a9cbf313b7d3396ee71a83e6d5
http://arxiv.org/abs/2001.11107
http://arxiv.org/abs/2001.11107
Reynolds-averaged Navier-Stokes (RANS) equations are presently one of the most popular models for simulating turbulence. Performing RANS simulation requires additional modeling for the anisotropic Reynolds stress tensor, but traditional Reynolds stre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f7b305101a803aab94156acad17556c
http://arxiv.org/abs/1909.03591
http://arxiv.org/abs/1909.03591
Publikováno v:
Mechanics Research Communications. 112:103614
The variational multiscale (VMS) formulation is used to develop residual-based VMS large eddy simulation (LES) models for Rayleigh-Benard convection. The resulting model is a mixed model that incorporates the VMS model and an eddy viscosity model. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68b0c71ee97ea66ead69821a9c9d0840
https://doi.org/10.1016/j.mechrescom.2020.103614
https://doi.org/10.1016/j.mechrescom.2020.103614
Publikováno v:
Journal of Fluid Mechanics. 784:565-595
Steady flows that optimize heat transport are obtained for two-dimensional Rayleigh–Bénard convection with no-slip horizontal walls for a variety of Prandtl numbers $\mathit{Pr}$ and Rayleigh number up to $\mathit{Ra}\sim 10^{9}$. Power-law scalin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd7a966095169df8d5c385765455727d
https://doi.org/10.1017/jfm.2015.615
https://doi.org/10.1017/jfm.2015.615