Biblio
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Author Title Type [ Year
] Filters: Keyword is Inverse problems [Clear All Filters]
, “Robust inversion, dimensionality reduction, and randomized sampling”, Mathematical Programming, vol. 134, pp. 101-125, 2012.
, “A penalty method for PDE-constrained optimization in inverse problems”, Inverse Problems, vol. 32, p. 015007, 2015.
, “Domain-specific abstractions for full-waveform inversion”, in SIAM Conference on Computational Science and Engineering, 2017.
, “Intersections and sums of sets for the regularization of inverse problems”, in Canadian Mathematical Society Winter Meeting, 2018.
, “Combining checkpointing and data compression to accelerate adjoint-based optimization problems”, Euro-Par 2019: Parallel Processing, pp. 87-100, 2019.
, “Generalized Minkowski sets for the regularization of inverse problems”, in SIAM Conference on Mathematical and Computational Issues in the Geosciences, 2019.
, “Deep Bayesian Inference for Task-based Seismic Imaging”, in KAUST, 2021.
, “Preconditioned training of normalizing flows for variational inference in inverse problems”, in 3rd Symposium on Advances in Approximate Bayesian Inference, 2021.
, “Adjoint operators as summary functions in amortized Bayesian inference frameworks”, ML4SEISMIC Partners Meeting. 2022.
, “Deep generative models for solving geophysical inverse problems”, Georgia Institute of Technology, Atlanta, 2022.
, “Low-cost uncertainty quantification for large-scale inverse problems”, ML4SEISMIC Partners Meeting. 2022.
, “Normalizing flows for regularization of 3D seismic inverse problems”, ML4SEISMIC Partners Meeting. 2022.
, “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.
, “Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification”, in SPIE Medical Imaging Conference, 2023.
, “Introduction to Seismic Laboratory for Imaging and Modeling”, CSE Student Recruiting Event. 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.
, “Solving PDE-based inverse problems with learned surrogates and constraints”, HotCSE Seminar. 2023.