20 citations · 135 across the 34 of their papers we have counts for
35 papers
Distributed Proximal Stein Variational Gradient Descent Algorithm for Large-scale Bayesian Inference in Traveltime Tomography
Akshay Vishwakarma, Kamal Aghazade, Ali Siahkoohi +1
We present a distributed framework for large-scale Bayesian inverse problems governed by the eikonal equation, with a specific focus on seismic traveltime tomography. Traditional d…
Amortized quadrature for posterior expectations in inverse problems
Ali Siahkoohi
Uncertainty in the solution of an inverse problem and in the tasks performed on it is quantified by posterior expectations, each an average of an integrand over posterior sampl…
Scalable Bayesian full waveform inversion via dual augmented Lagrangian SVGD
Kamal Aghazade, Ali Siahkoohi, Ali Gholami
Full waveform inversion is an ill-posed inverse problem whose solution non-uniqueness -- i.e., arising from band-limited, finite-aperture, noisy data -- calls for uncertainty quant…
On the role of memorization in learned priors for geophysical inverse problems
Ali Siahkoohi, Davide Sabeddu
Learned priors based on deep generative models offer data-driven regularization for seismic inversion, but training them requires a dataset of representative subsurface models -- a…
Dual-space posterior sampling for Bayesian inference in constrained inverse problems
Ali Siahkoohi, Kamal Aghazade, Ali Gholami
Inverse problems constrained by partial differential equations are often ill-conditioned due to noisy, incomplete data or inherent non-uniqueness. A prominent example is full wavef…
Conditional neural control variates for variance reduction in Bayesian inverse problems
Ali Siahkoohi, Hyunwoo Oh
Bayesian inference for inverse problems involves computing expectations under posterior distributions--e.g., posterior means, variances, or predictive quantities--typically via Mon…
Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems
Lorenzo Baldassari, Ali Siahkoohi, Josselin Garnier +2
This work introduces a sampling method capable of solving Bayesian inverse problems in function space. It does not assume the log-concavity of the likelihood, meaning that it is co…
ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems
Rafael Orozco, Ali Siahkoohi, Mathias Louboutin +1
Due to their uncertainty quantification, Bayesian solutions to inverse problems are the framework of choice in applications that are risk averse. These benefits come at the cost of…
InvertibleNetworks.jl: A Julia package for scalable normalizing flows
Rafael Orozco, Philipp Witte, Mathias Louboutin +4
InvertibleNetworks.jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributio…
3D seismic survey design by maximizing the spectral gap
Yijun Zhang, Ziyi Yin, Oscar López +3
The massive cost of 3D acquisition calls for methods to reduce the number of receivers by designing optimal receiver sampling masks. Recent studies on 2D seismic showed that maximi…
Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics
Rafael Orozco, Ali Siahkoohi, Mathias Louboutin +1
We present an iterative framework to improve the amortized approximations of posterior distributions in the context of Bayesian inverse problems, which is inspired by loop-unrolled…
Conditional score-based diffusion models for Bayesian inference in infinite dimensions
Lorenzo Baldassari, Ali Siahkoohi, Josselin Garnier +2
Since their initial introduction, score-based diffusion models (SDMs) have been successfully applied to solve a variety of linear inverse problems in finite-dimensional vector spac…
Multi-scale clustering and source separation of InSight mission seismic data
Ali Siahkoohi, Rudy Morel, Randall Balestriero +4
Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only acc…
Learned multiphysics inversion with differentiable programming and machine learning
Mathias Louboutin, Ziyi Yin, Rafael Orozco +7
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems invol…
Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification
Rafael Orozco, Mathias Louboutin, Ali Siahkoohi +3
We present a novel approach to transcranial ultrasound computed tomography that utilizes normalizing flows to improve the speed of imaging and provide Bayesian uncertainty quantifi…
Optimized time-lapse acquisition design via spectral gap ratio minimization
Yijun Zhang, Ziyi Yin, Oscar Lopez +4
Modern-day reservoir management and monitoring of geological carbon storage increasingly call for costly time-lapse seismic data collection. In this letter, we show how techniques…
Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data
Ali Siahkoohi, Rudy Morel, Maarten V. de Hoop +3
Source separation involves the ill-posed problem of retrieving a set of source signals that have been observed through a mixing operator. Solving this problem requires prior knowle…
Reliable amortized variational inference with physics-based latent distribution correction
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco +1
Bayesian inference for high-dimensional inverse problems is computationally costly and requires selecting a suitable prior distribution. Amortized variational inference addresses t…
A simulation-free seismic survey design by maximizing the spectral gap
Yijun Zhang, Mathias Louboutin, Ali Siahkoohi +3
Due to the tremendous cost of seismic data acquisition, methods have been developed to reduce the amount of data acquired by designing optimal missing trace reconstruction algorith…
Wave-equation-based inversion with amortized variational Bayesian inference
Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti +1
Solving inverse problems involving measurement noise and modeling errors requires regularization in order to avoid data overfit. Geophysical inverse problems, in which the Earth's…
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…
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
Ziyi Yin, Ali Siahkoohi, Mathias Louboutin +1
Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solver…
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann
Seismic imaging is an ill-posed inverse problem that is challenged by noisy data and modeling inaccuracies -- due to errors in the background squared-slowness model. Uncertainty qu…
Deep Bayesian inference for seismic imaging with tasks
Ali Siahkoohi, Gabrio Rizzuti, Felix J. Herrmann
We propose to use techniques from Bayesian inference and deep neural networks to translate uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as ho…