5 citations · 16 across the 5 of their papers we have counts for
5 papers
BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows a case study in optimal monitor well placement for CO sequestration
Rafael Orozco, Abhinav Gahlot, Felix J. Herrmann
CO sequestration is a crucial engineering solution for mitigating climate change. However, the uncertain nature of reservoir properties, necessitates rigorous monitoring of CO$…
A Digital Twin for Geological Carbon Storage with Controlled Injectivity
Abhinav Prakash Gahlot, Haoyun Li, Ziyi Yin +2
We present an uncertainty-aware Digital Twin (DT) for geologic carbon storage (GCS), capable of handling multimodal time-lapse data and controlling CO2 injectivity to mitigate rese…
Probabilistic Bayesian optimal experimental design using conditional normalizing flows
Rafael Orozco, Felix J. Herrmann, Peng Chen
Bayesian optimal experimental design (OED) seeks to conduct the most informative experiment under budget constraints to update the prior knowledge of a system to its posterior from…
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…
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…