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pro vyhledávání: '"Farthing, Matthew W"'
Numerous applications in biology, statistics, science, and engineering require generating samples from high-dimensional probability distributions. In recent years, the Hamiltonian Monte Carlo (HMC) method has emerged as a state-of-the-art Markov chai
Externí odkaz:
http://arxiv.org/abs/2405.05033
Autor:
Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew W., Hesser, Tyler, Kitanidis, Peter K., Darve, Eric F.
Fast and reliable prediction of river flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for this purpose. However, traditional numerical solvers of the SWEs are co
Externí odkaz:
http://arxiv.org/abs/2111.11702
Autor:
Dutta, Sourav, Rivera-Casillas, Peter, Cecil, Orie M., Farthing, Matthew W., Perracchione, Emma, Putti, Mario
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:
http://arxiv.org/abs/2107.02784
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:
http://arxiv.org/abs/2104.13962
Autor:
Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew W., Hesser, Tyler, Kitanidis, Peter K., Darve, Eric F.
Fast and reliable prediction of riverine flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for prediction of the flow velocities. However, accurate and fast predic
Externí odkaz:
http://arxiv.org/abs/2012.02620
Publikováno v:
Journal of Computational Physics, vol. 439, p. 110378, 2021
In this work, we develop Non-Intrusive Reduced Order Models (NIROMs) that combine Proper Orthogonal Decomposition (POD) with a Radial Basis Function (RBF) interpolation method to construct efficient reduced order models for time-dependent problems ar
Externí odkaz:
http://arxiv.org/abs/2002.11329
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Publikováno v:
In Journal of Computational Physics 15 August 2021 439
Publikováno v:
In Journal of Computational Physics 1 January 2021 424
Autor:
Lin, Lin, Weigand, Timothy M., Farthing, Matthew W., Jutaporn, Panitan, Miller, Cass T., Coronell, Orlando
Publikováno v:
In Journal of Membrane Science 15 October 2018 564:935-944