research
∙
04/12/2023
Learned multiphysics inversion with differentiable programming and machine learning
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (S...
share
research
∙
11/23/2022
SciAI4Industry – Solving PDEs for industry-scale problems with deep learning
Solving partial differential equations with deep learning makes it possi...
research
∙
04/04/2022
Towards Large-Scale Learned Solvers for Parametric PDEs with Model-Parallel Fourier Neural Operators
Fourier neural operators (FNOs) are a recently introduced neural network...
research
∙
03/28/2022
Accelerating innovation with software abstractions for scalable computational geophysics
We present the SLIM (https://github.com/slimgroup) open-source software ...
research
∙
01/11/2021
Preconditioned training of normalizing flows for variational inference in inverse problems
Obtaining samples from the posterior distribution of inverse problems wi...
research
∙
07/15/2020
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
In inverse problems, we often have access to data consisting of paired s...
research
∙
04/16/2020
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
Uncertainty quantification for full-waveform inversion provides a probab...
research
∙
11/27/2019
Serverless seismic imaging in the cloud
This abstract presents a serverless approach to seismic imaging in the c...
research
∙
09/03/2019
An Event-Driven Approach to Serverless Seismic Imaging in the Cloud
Adapting the cloud for high-performance computing (HPC) is a challenging...
research
∙
08/06/2018
