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Zhilong Fang, Chia Ying Lee, Curt Da Silva, Tristan van Leeuwen, and Felix J. Herrmann, “Uncertainty quantification for Wavefield Reconstruction Inversion using a PDE free semidefinite Hessian and randomize-then-optimize method”, in SEG Technical Program Expanded Abstracts, 2016, pp. 1390-1394.
Zhilong Fang and Felix J. Herrmann, “A stochastic quasi-Newton McMC method for uncertainty quantification of full-waveform inversion”, UBC, TR-EOAS-2014-6, 2014.
Zhilong Fang, Curt Da Silva, and Felix J. Herrmann, “Fast uncertainty quantification for 2D full-waveform inversion with randomized source subsampling”, in EAGE Annual Conference Proceedings, 2014.
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Abhinav Prakash Gahlot, Rafael Orozco, Haoyun Li, Grant Bruer, Ziyi Yin, Mathias Louboutin, and Felix J. Herrmann, “An Uncertainty-Aware Digital Twin for Geological Carbon Storage”, in SIAM Conference on Uncertainty Quantification, 2024.
Abhinav Prakash Gahlot, Haoyun Li, Ziyi Yin, Rafael Orozco, and Felix J. Herrmann, “A Digital Twin for Geological Carbon Storage with Controlled Injectivity”. 2024.
Abhinav Prakash Gahlot, Rafael Orozco, Haoyun Li, Huseyin Tuna Erdinc, Ziyi Yin, Mathias Louboutin, and Felix J. Herrmann, “Digital Twins in the Era of Generative AI: Application to Geological CO 2 Storage”, HCMF Seminar. 2024.
Abhinav Prakash Gahlot, Ting-ying Yu, Rafael Orozco, Ziyi Yin, Mathias Louboutin, and Felix J. Herrmann, “Monitoring subsurface CO2 plumes with learned sequential Bayesian inference”, ML4SEISMIC Partners Meeting. 2023.
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Felix J. Herrmann, Mathias Louboutin, and Ali Siahkoohi, “ML@scale using randomized linear algebra”, in Microsoft, 2021.
Felix J. Herrmann, Ali Siahkoohi, and Gabrio Rizzuti, “Learned imaging with constraints and uncertainty quantification”, HotCSE Seminar. 2019.
Felix J. Herrmann, Abhinav Prakash Gahlot, Rafael Orozco, Ziyi Yin, and Haoyun Li, “DT4GCS –- Digital Twin for Geological CO2 Storage and Control”, in Gigatonnes CO2 Storage Workshop, 2024.
Felix J. Herrmann, Ali Siahkoohi, and Gabrio Rizzuti, “Learned imaging with constraints and uncertainty quantification”, in Neural Information Processing Systems (NeurIPS), 2019.
Felix J. Herrmann, “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.
Felix J. Herrmann, Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti, Philipp A. Witte, and Mathias Louboutin, “Learned wave-based imaging - variational inference at scale”, in Delft, 2021.
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Mathias Louboutin, Ali Siahkoohi, Ziyi Yin, Rafael Orozco, Thomas J. Grady II, Yijun Zhang, Philipp A. Witte, Gabrio Rizzuti, and Felix J. Herrmann, “Abstractions for at-scale seismic inversion”, in Rice Oil and Gas High Performance Computing Conference 2022, 2022, p. Thursday Workshop: Devito Training and Hackathon.
Mathias Louboutin, Philipp A. Witte, Ali Siahkoohi, Gabrio Rizzuti, Ziyi Yin, Rafael Orozco, and Felix J. Herrmann, “Accelerating innovation with software abstractions for scalable computational geophysics”, in International Meeting for Applied Geoscience and Energy Expanded Abstracts, 2022.
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Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Fast neural FWI with amortized uncertainty quantification”, in International Meeting for Applied Geoscience and Energy, 2023.
Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Generative Seismic Kriging with Normalizing Flows”, in International Meeting for Applied Geoscience and Energy, 2023.
Rafael Orozco, Abhinav Prakash Gahlot, and Felix J. Herrmann, “BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows - a case study in optimal monitor well placement for CO2 sequestration”. 2024.
Rafael Orozco, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, and Felix J. Herrmann, “Amortized normalizing flows for transcranial ultrasound with uncertainty quantification”, in Medical Imaging with Deep Learning, 2023.
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.
Rafael Orozco, Mathias Louboutin, Peng Chen, and Felix J. Herrmann, “Uncertainty quantification so what? Leveraging probabilistic seismic inversion for experimental design”, ML4SEISMIC Partners Meeting. 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.
Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Normalizing flows for regularization of 3D seismic inverse problems”, ML4SEISMIC Partners Meeting. 2022.
Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Towards generative seismic kriging with normalizing flows”, ML4SEISMIC Partners Meeting. 2023.
Rafael Orozco, Philipp A. Witte, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Bas Peters, and Felix J. Herrmann, “InvertibleNetworks.jl: A Julia package for scalable normalizing flows”. 2023.

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