no code implementations • 8 May 2024 • Dhruv V. Patel, Jonghyun Lee, Matthew W. Farthing, Peter K. Kitanidis, Eric F. Darve
In this multi-fidelity algorithm, the acceptance probability is computed in the first stage via a standard HMC proposal using an inexpensive differentiable surrogate model, and if the proposal is accepted, the posterior is evaluated in the second stage using the high-fidelity (HF) numerical solver.
no code implementations • 23 Nov 2021 • Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew W. Farthing, Tyler Hesser, Peter K. Kitanidis, Eric F. Darve
Furthermore, we augment the bathymetry posterior distribution to a more general class of distributions before providing them as inputs to ML algorithm in the second stage.
2 code implementations • 6 Jul 2021 • Sourav Dutta, Peter Rivera-Casillas, Orie M. Cecil, Matthew W. Farthing, Emma Perracchione, Mario Putti
Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields.
2 code implementations • 22 Apr 2021 • Sourav Dutta, Peter Rivera-Casillas, Matthew W. Farthing
Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields.
1 code implementation • 4 Dec 2020 • Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew W. Farthing, Tyler Hesser, Peter K. Kitanidis, Eric F. Darve
First, using the principal component geostatistical approach (PCGA) we estimate the probability density function of the bathymetry from flow velocity measurements, and then we use multiple machine learning algorithms to obtain a fast solver of the SWEs, given augmented realizations from the posterior bathymetry distribution and the prescribed range of BCs.