Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Peter Rivera"'
Autor:
Adam M. Collins, Peter Rivera-Casillas, Sourav Dutta, Orie M. Cecil, Andrew C. Trautz, Matthew W. Farthing
Publikováno v:
Frontiers in Water, Vol 5 (2023)
The goal of this study is to leverage emerging machine learning (ML) techniques to develop a framework for the global reconstruction of system variables from potentially scarce and noisy observations and to explore the epistemic uncertainty of these
Externí odkaz:
https://doaj.org/article/1edc3b568e154c7d869b747b3728f741
Publikováno v:
Mathematical and Computational Applications, Vol 27, Iss 3, p 34 (2022)
Physical systems governed by advection-dominated partial differential equations (PDEs) are found in applications ranging from engineering design to weather forecasting. They are known to pose severe challenges to both projection-based and non-intrusi
Externí odkaz:
https://doaj.org/article/a3c0bff0754b43be9277550fdc99144e
Autor:
Peter Rivera, Marc Chun
Publikováno v:
Educational Policy. 37:101-121
In this paper two foundation program officers describe their experiences as funders engaged in supporting research practice partnerships in two very distinct settings. The authors set the context for their experiences and walk through the challenges
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2fa0f80c6ed9f01eb335f9ee68bb193
https://doi.org/10.1177/08959048221134585
https://doi.org/10.1177/08959048221134585
Autor:
Orie M. Cecil, Peter Rivera-Casillas, Matthew W. Farthing, Mario Putti, Emma Perracchione, Sourav Dutta
Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. In a previous work [arXiv:2104.13962], we explored the use of Neural Ordinary Differential Equations (NODE) as a non-int
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f581406092eb751b454981cd320b70c
http://arxiv.org/abs/2107.02784
http://arxiv.org/abs/2107.02784
Publikováno v:
Scopus-Elsevier
Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. Here, we explore the use of Neural Ordinary Differential Equations, a recently introduced family of continuous-depth, di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8e80fb8a1bf6134421dae6bccdd0464
Publikováno v:
Software Impacts. 10:100129
Modern reduced order models (ROMs) have widespread applicability in computational science and engineering as they allow accurate simulation of complex, nonlinear problems with minimal computational cost. In this paper, we introduce a Python-based imp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::192f9c39248ce4ef1840fcaca3e8fdba
https://doi.org/10.1016/j.simpa.2021.100129
https://doi.org/10.1016/j.simpa.2021.100129
Autor:
Claudia Grauf-Grounds, Tina Sellers, Scott A. Edwards, Hee-Sun Cheon, Don Macdonald, Shawn Whitney, Peter Rivera
A Practice Beyond Cultural Humility offers specific guidance to support students and practitioners in providing on-going, culturally-attuned professional care. The book introduces a multicultural diversity-training model named the ORCA-Stance, an int
Autor:
Peter Rivera
Publikováno v:
Credit Derivative Strategies: New Thinking on Managing Risk and Return
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a97febdc2244718bf4bafa0789a6bf9
https://doi.org/10.1002/9781119204220.ch11
https://doi.org/10.1002/9781119204220.ch11