Biblio
“A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification”, in EAGE Annual Conference Proceedings, 2020.
, “Deep generative models for solving geophysical inverse problems”, Georgia Institute of Technology, Atlanta, 2022.
, “Deep Convolutional Neural Networks in prestack seismic–-two exploratory examples”, in SEG Technical Program Expanded Abstracts, 2018, pp. 2196-2200.
, “Deep Bayesian Inference for Task-based Seismic Imaging”, in KAUST, 2021.
, “Deep Bayesian inference for seismic imaging with tasks”, Geophysics, vol. 87, pp. 281-302, 2022.
, “Capturing velocity-model uncertainty and two-phase flow with Fourier Neural Operators”, in EAGE Annual Conference Proceedings, 2022, p. AI in Geoscience and Geophysics: Current Trends and Future Prospects (Dedicated Session).
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“Amortized velocity continuation with Fourier neural operators”, ML4SEISMIC Partners Meeting. 2022.
, “Amortized normalizing flows for transcranial ultrasound with uncertainty quantification”, in Medical Imaging with Deep Learning, 2023.
, “Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification”, in SPIE Medical Imaging Conference, 2023.
, “Adjoint operators as summary functions in amortized Bayesian inference frameworks”, ML4SEISMIC Partners Meeting. 2022.
, “Accelerating innovation with software abstractions for scalable computational geophysics”, in International Meeting for Applied Geoscience and Energy Expanded Abstracts, 2022.
, “Abstractions for at-scale seismic inversion”, in Rice Oil and Gas High Performance Computing Conference 2022, 2022, p. Thursday Workshop: Devito Training and Hackathon.
, “3D seismic survey design by maximizing the spectral gap”, in International Meeting for Applied Geoscience and Energy, 2023.
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