Biblio
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] Filters: Keyword is Uncertainty quantification [Clear All Filters]
, “Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering”, ML4SEISMIC Partners Meeting. 2024.
, “Solving geophysical inverse problems with scientific machine learning”, Georgia Institute of Technology, Atlanta, 2024.
, “An Uncertainty-Aware Digital Twin for Geological Carbon Storage”, in SIAM Conference on Uncertainty Quantification, 2024.
, “Velocity Continuation for Common Image Gathers with Fourier Neural Operators”, in International Meeting for Applied Geoscience and Energy, 2024.
, “WISE: full-Waveform variational Inference via Subsurface Extensions”, Geophysics, vol. 89, 2024.
, “WISER: full-Waveform variational Inference via Subsurface Extensions with Refinements”, in International Meeting for Applied Geoscience and Energy, 2024.
, “Act normal that's crazy enough — an overview of seismic inversion with normalizing flows and surrogate modeling”, Scientific Computing, Applied and Industrial Mathematics (SCAIM) Seminar. 2023.
, “Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification”, in SPIE Medical Imaging Conference, 2023.
, “Amortized Bayesian Full Waveform Inversion and Experimental Design with Normalizing Flows”, in International Meeting for Applied Geoscience and Energy, 2023.
, “Amortized normalizing flows for transcranial ultrasound with uncertainty quantification”, in Medical Imaging with Deep Learning, 2023.
, “Fast neural FWI with amortized uncertainty quantification”, in International Meeting for Applied Geoscience and Energy, 2023.
, “Generative Seismic Kriging with Normalizing Flows”, in International Meeting for Applied Geoscience and Energy, 2023.
, “Introduction to Seismic Laboratory for Imaging and Modeling”, CSE Student Recruiting Event. 2023.
, “Monitoring subsurface CO2 plumes with learned sequential Bayesian inference”, ML4SEISMIC Partners Meeting. 2023.
, “Monitoring Subsurface CO2 Plumes with Sequential Bayesian Inference”, in International Meeting for Applied Geoscience and Energy, 2023.
, “Reliable amortized variational inference with physics-based latent distribution correction”, Geophysics, vol. 88, 2023.
, “Towards generative seismic kriging with normalizing flows”, ML4SEISMIC Partners Meeting. 2023.
, “Uncertainty quantification so what? Leveraging probabilistic seismic inversion for experimental design”, ML4SEISMIC Partners Meeting. 2023.
, “Abstractions for at-scale seismic inversion”, in Rice Oil and Gas High Performance Computing Conference 2022, 2022, p. Thursday Workshop: Devito Training and Hackathon.
, “Accelerating innovation with software abstractions for scalable computational geophysics”, in International Meeting for Applied Geoscience and Energy Expanded Abstracts, 2022.
, “Adjoint operators as summary functions in amortized Bayesian inference frameworks”, ML4SEISMIC Partners Meeting. 2022.
, “Amortized velocity continuation with Fourier neural operators”, ML4SEISMIC Partners Meeting. 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).
, “Deep Bayesian inference for seismic imaging with tasks”, Geophysics, vol. 87, pp. 281-302, 2022.