Biblio
] , “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 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.
, “Extended source imaging –- a unifying framework for seismic and medical imaging”, in SEG Technical Program Expanded Abstracts, 2020, pp. 3502-3506.
, “Fast and reliability-aware seismic imaging with conditional normalizing flows”, in Intelligent illumination of the Earth, 2021.
, “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.
, “Inference of CO2 flow patterns – a feasibility study”, in Neural Information Processing Systems (NeurIPS), 2023.
, “Learned non-linear simultenous source and corresponding supershot for seismic imaging.”, in International Meeting for Applied Geoscience and Energy, 2023.
, “Learned wave-based imaging - variational inference at scale”, in Delft, 2021.
, “Monitoring Subsurface CO2 Plumes with Sequential Bayesian Inference”, in International Meeting for Applied Geoscience and Energy, 2023.
, “Normalizing Flows for Bayesian Experimental Design in Imaging Applications”, in SIAM Conference on Uncertainty Quantification, 2024.
, “Photoacoustic Imaging with Conditional Priors from Normalizing Flows”, in Neural Information Processing Systems (NeurIPS), 2021.
, “Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics”, in 5th Symposium on Advances in Approximate Bayesian Inference, 2023.
, “Reliable amortized variational inference with conditional normalizing flows via physics-based latent distribution correction”, in IMAGE Workshop on Subsurface Uncertainty Description and Estimation - Moving Away from Single Prediction with Distribution Learning, 2022.
, “An Uncertainty-Aware Digital Twin for Geological Carbon Storage”, in SIAM Conference on Uncertainty Quantification, 2024.
, “Uncertainty-aware time-lapse monitoring of geological carbon storage with learned surrogates”, in Engineering Mechanics Institute Conference, 2023.
, “Wave-equation based inversion with amortized variational Bayesian inference”, in EAGE Annual Conference Proceedings, 2022, p. Session 2: Velocity model building and imaging (different domains).
, “Learned multiphysics inversion with differentiable programming and machine learning”, The Leading Edge, vol. 42, pp. 452-516, 2023.
, “Reliable amortized variational inference with physics-based latent distribution correction”, Geophysics, vol. 88, 2023.
, “Solving multiphysics-based inverse problems with learned surrogates and constraints”, Advanced Modeling and Simulation in Engineering Sciences, vol. 10, 2023.
, “WISE: full-Waveform variational Inference via Subsurface Extensions”, Geophysics, 2024.
, “Adjoint operators as summary functions in amortized Bayesian inference frameworks”, ML4SEISMIC Partners Meeting. 2022.