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2024
Huseyin Tuna Erdinc, Rafael Orozco, and Felix J. Herrmann, “SAGE – Subsurface foundational model with AI-driven Geostatical Extraction”, ML4SEISMIC Partners Meeting. 2024.
Grant Bruer, Abhinav Prakash Gahlot, Edmond Chow, and Felix J. Herrmann, “Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering”, ML4SEISMIC Partners Meeting. 2024.
I. Bhar, Abhinav Prakash Gahlot, and Felix J. Herrmann, “Sensitivity of Horizontal shear waves on Geological Carbon Storage”, ML4SEISMIC Partners Meeting. 2024.
Richard Rex, Avasarala, S., Grady, T., and Felix J. Herrmann, “Tucker Compression for Scalable Operator Learning in Large-Scale Parametric PDE Models”, ML4SEISMIC Partners Meeting. 2024.
Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “WISE: full-Waveform variational Inference via Subsurface Extensions”, Geophysics, vol. 89, 2024.
Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “WISER: full-Waveform variational Inference via Subsurface Extensions with Refinements”, in International Meeting for Applied Geoscience and Energy, 2024.
2023
Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, and Felix J. Herrmann, “Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification”, in SPIE Medical Imaging Conference, 2023.
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, and Felix J. Herrmann, “Reliable amortized variational inference with physics-based latent distribution correction”, Geophysics, vol. 88, 2023.
Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Solving multiphysics-based inverse problems with learned surrogates and constraints”, Advanced Modeling and Simulation in Engineering Sciences, vol. 10, 2023.
Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Solving PDE-based inverse problems with learned surrogates and constraints”, HotCSE Seminar. 2023.
2022
Rafael Orozco, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann, “Adjoint operators as summary functions in amortized Bayesian inference frameworks”, ML4SEISMIC Partners Meeting. 2022.
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, and Felix J. Herrmann, “Low-cost uncertainty quantification for large-scale inverse problems”, ML4SEISMIC Partners Meeting. 2022.
Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Normalizing flows for regularization of 3D seismic inverse problems”, ML4SEISMIC Partners Meeting. 2022.
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, and Felix J. Herrmann, “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.
2021
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A. Witte, and Felix J. Herrmann, “Deep Bayesian Inference for Task-based Seismic Imaging”, in KAUST, 2021.
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A. Witte, and Felix J. Herrmann, “Preconditioned training of normalizing flows for variational inference in inverse problems”, in 3rd Symposium on Advances in Approximate Bayesian Inference, 2021.
2019
Bas Peters and Felix J. Herrmann, “Generalized Minkowski sets for the regularization of inverse problems”, in SIAM Conference on Mathematical and Computational Issues in the Geosciences, 2019.
2018
Bas Peters and Felix J. Herrmann, “Intersections and sums of sets for the regularization of inverse problems”, in Canadian Mathematical Society Winter Meeting, 2018.
2017
Felix J. Herrmann and Curt Da Silva, “Domain-specific abstractions for full-waveform inversion”, in SIAM Conference on Computational Science and Engineering, 2017.
2015
Tristan van Leeuwen and Felix J. Herrmann, “A penalty method for PDE-constrained optimization in inverse problems”, Inverse Problems, vol. 32, p. 015007, 2015.
2012
Aleksandr Y. Aravkin, Michael P. Friedlander, Felix J. Herrmann, and Tristan van Leeuwen, “Robust inversion, dimensionality reduction, and randomized sampling”, Mathematical Programming, vol. 134, pp. 101-125, 2012.

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