202 citations · 339 across the 14 of their papers we have counts for
3 papers · 1 filter
SciAI4Industry -- Solving PDEs for industry-scale problems with deep learning
Philipp A. Witte, Russell J. Hewett, Kumar Saurabh +2
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…
Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs
Thomas J. Grady, Rishi Khan, Mathias Louboutin +5
Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been show…
Accelerating innovation with software abstractions for scalable computational geophysics
Mathias Louboutin, Philipp A. Witte, Ali Siahkoohi +4
We present the SLIM (https://github.com/slimgroup) open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g…