335 citations · 1.8k across the 47 of their papers we have counts for
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Monitoring of water volume in a porous reservoir using seismic data: A 3D simulation study
Mahnaz Khalili, Peter Göransson, Jan S. Hesthaven +3
A potential framework to estimate the volume of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the man-made groundwater reservoir i…
Constraint-Aware Neural Networks for Riemann Problems
Jim Magiera, Deep Ray, Jan S. Hesthaven +1
Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical…
Estimation of groundwater storage from seismic data using deep learning
Timo Lähivaara, Alireza Malehmir, Antti Pasanen +3
Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components such as the amount of groundwate…
Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography
Timo Lähivaara, Leo Kärkkäinen, Janne M. J. Huttunen +1
We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the…
Projective multiscale time-integration for electrostatic particle-in-cell methods
Paul Cazeaux, Jan S. Hesthaven
The simulation of problems in kinetic plasma physics are often challenging due to strongly coupled phenomena across multiple scales. In this work, we propose a wavelet-based coarse…