Biblio
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] Year Filters: Author is Mathias Louboutin and Keyword is deep learning [Clear All Filters]
, “Abstractions and algorithms for efficient seismic inversion on accelerators”, in IMAGE Workshop on What's Next for FWI and its Derived Products, 2022.
, “Enhancing CO2 Leakage Detectability via Dataset Augmentation”, in International Meeting for Applied Geoscience and Energy, 2023.
, “InvertibleNetworks.jl - Memory efficient deep learning in Julia”, in JuliaCon, 2021.
, “Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators”, in International Meeting for Applied Geoscience and Energy Expanded Abstracts, 2022.
, “Learned non-linear simultenous source and corresponding supershot for seismic imaging.”, in International Meeting for Applied Geoscience and Energy, 2023.
, “ML@scale using randomized linear algebra”, in Microsoft, 2021.
, “Monitoring Subsurface CO2 Plumes with Sequential Bayesian Inference”, in International Meeting for Applied Geoscience and Energy, 2023.
, “The Next Step: Interoperable Domain-Specific Programming”, in SIAM Conference on Computational Science and Engineering, 2023.
, “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.
, “Unsupervised data-guided uncertainty analysis in imaging and horizon tracking”, in SIAM Texas-Louisiana, 2020.
, “WISER: full-Waveform variational Inference via Subsurface Extensions with Refinements”, in International Meeting for Applied Geoscience and Energy, 2024.
, “The importance of transfer learning in seismic modeling and imaging”, Geophysics, 2019.
, “Learned multiphysics inversion with differentiable programming and machine learning”, The Leading Edge, vol. 42, pp. 452-516, 2023.
, “Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs”, Computers & Geosciences, vol. 178, p. 105402, 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.
, “Digital Twins in the Era of Generative AI: Application to Geological CO 2 Storage”, HCMF Seminar. 2024.
, “Effective scaling of numerical surrogates via domain-decomposed Fourier neural operators”, ML4SEISMIC Partners Meeting. 2022.
, “Generative AI for full-waveform variational inference”, Georgia Tech Geophysics Seminar. 2024.
, “Improved automatic seismic CO2 leakage detection via dataset augmentation”, ML4SEISMIC Partners Meeting. 2023.
, “Learned extensions for wave-based simulation and inversion”, ML4SEISMIC Partners Meeting. 2022.
, “Monitoring subsurface CO2 plumes with learned sequential Bayesian inference”, ML4SEISMIC Partners Meeting. 2023.
, “Multifidelity conditional normalizing flows for physics-guided Bayesian inference”, ML4SEISMIC Partners Meeting. 2021.