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Found 21 papers, 13 papers with code
ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems
The benefits of our method requires extra computations but these remain frugal since they are based on physics-hybrid methods and summary statistics.
InvertibleNetworks.jl: A Julia package for scalable normalizing flows
InvertibleNetworks. jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributions.
WISE: full-Waveform variational Inference via Subsurface Extensions
We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging.
Solving multiphysics-based inverse problems with learned surrogates and constraints
Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically.
Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics
We validate our method in a controlled setting by applying it to a stylized problem, and observe improved posterior approximations with each iteration.
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.
Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification
Our method combines physics-informed methods and data-driven methods to accelerate the reconstruction of the final image.
De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images
With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology.
De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection
Amongst the different monitoring modalities, seismic imaging stands out with its ability to attain high resolution and high fidelity images.
Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging
Photoacoustic imaging (PAI) can image high-resolution structures of clinical interest such as vascularity in cancerous tumor monitoring.
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.
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
As such, the main contribution of this work is a survey-specific Fourier neural operator surrogate to velocity continuation that maps seismic images associated with one background model to another virtually for free.
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
We show that we can accurately use a Fourier neural operator as a proxy for the fluid-flow simulator for a fraction of the computational cost.
Low-memory stochastic backpropagation with multi-channel randomized trace estimation
Thanks to the combination of state-of-the-art accelerators and highly optimized open software frameworks, there has been tremendous progress in the performance of deep neural networks.
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.
Lossy Checkpoint Compression in Full Waveform Inversion: a case study with ZFPv0.5.5 and the Overthrust Model
This paper proposes a new method that combines check-pointing methods with error-controlled lossy compression for large-scale high-performance Full-Waveform Inversion (FWI), an inverse problem commonly used in geophysical exploration.
Computational Physics Numerical Analysis Numerical Analysis
Scaling through abstractions -- high-performance vectorial wave simulations for seismic inversion with Devito
[Devito] is an open-source Python project based on domain-specific language and compiler technology.
Neural network augmented wave-equation simulation
One proxy of incomplete physics is an inaccurate discretization of Laplacian in simulation of wave equation via finite-difference method.
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
Architecture and performance of Devito, a system for automated stencil computation
Some of these are obtained through well-established stencil optimizers, integrated in the back-end of the Devito compiler.
Mathematical Software 65N06, 68N20
