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Found 8 papers, 6 papers with code
Learned multiphysics inversion with differentiable programming and machine learning
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.
SciAI4Industry -- Solving PDEs for industry-scale problems with deep learning
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.
Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs
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.
Preconditioned training of normalizing flows for variational inference in inverse problems
Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneous Earth.
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
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.
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
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.
An Event-Driven Approach to Serverless Seismic Imaging in the Cloud
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
Devito: an embedded domain-specific language for finite differences and geophysical exploration
We introduce Devito, a new domain-specific language for implementing high-performance finite difference partial differential equation solvers.
Discrete Mathematics Geophysics
