1 code implementation • 23 Nov 2021 • Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew Farthing, Tyler Hesser, Peter K. Kitanidis, Eric F. Darve
Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements.
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.
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.
no code implementations • 19 Nov 2020 • Yizhou Qian, Mojtaba Forghani, Jonghyun Harry Lee, Matthew Farthing, Tyler Hesser, Peter Kitanidis, Eric Darve
We propose a Deep Neural Network (DNN) to compute posterior estimates of the nearshore bathymetry, as well as a conditional Generative Adversarial Network (cGAN) that samples from the posterior distribution.