1 code implementation • 12 Apr 2023 • Mathias Louboutin, Ziyi Yin, Rafael Orozco, Thomas J. Grady II, Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte, Olav Møyner, Gerard J. Gorman, Felix J. Herrmann
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e. g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations.
no code implementations • 23 Nov 2022 • Philipp A. Witte, Russell J. Hewett, Kumar Saurabh, AmirHossein Sojoodi, Ranveer Chandra
Solving partial differential equations with deep learning makes it possible to reduce simulation times by multiple orders of magnitude and unlock scientific methods that typically rely on large numbers of sequential simulations, such as optimization and uncertainty quantification.
1 code implementation • 4 Apr 2022 • Thomas J. Grady II, Rishi Khan, Mathias Louboutin, Ziyi Yin, Philipp A. Witte, Ranveer Chandra, Russell J. Hewett, Felix J. Herrmann
Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been shown to perform significantly better than comparable deep learning approaches.
2 code implementations • pproximateinference AABI Symposium 2021 • Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A. Witte, Felix J. Herrmann
Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneous Earth.
no code implementations • 15 Jul 2020 • Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte, Felix J. Herrmann
In inverse problems, we often have access to data consisting of paired samples $(x, y)\sim p_{X, Y}(x, y)$ where $y$ are partial observations of a physical system, and $x$ represents the unknowns of the problem.
2 code implementations • 16 Apr 2020 • Gabrio Rizzuti, Ali Siahkoohi, Philipp A. Witte, Felix J. Herrmann
Uncertainty quantification for full-waveform inversion provides a probabilistic characterization of the ill-conditioning of the problem, comprising the sensitivity of the solution with respect to the starting model and data noise.
1 code implementation • 3 Sep 2019 • Philipp A. Witte, Mathias Louboutin, Henryk Modzelewski, Charles Jones, James Selvage, Felix J. Herrmann
As an alternative to the generic lift and shift approach, we consider the specific application of seismic imaging and demonstrate a serverless and event-driven approach for running large-scale instances of this problem in the cloud.
Distributed, Parallel, and Cluster Computing Geophysics
4 code implementations • 6 Aug 2018 • Mathias Louboutin, Michael Lange, Fabio Luporini, Navjot Kukreja, Philipp A. Witte, Felix J. Herrmann, Paulius Velesko, Gerard J. Gorman
We introduce Devito, a new domain-specific language for implementing high-performance finite difference partial differential equation solvers.
Discrete Mathematics Geophysics