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W
Ziyi Yin, Rafael Orozco, and Felix J. Herrmann, “WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction”. 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.
Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “WISE: full-Waveform variational Inference via Subsurface Extensions”, Geophysics, 2024.
U
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.
R
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.
Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, and Felix J. Herrmann, “Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics”, in 5th Symposium on Advances in Approximate Bayesian Inference, 2023.
N
Rafael Orozco, Mathias Louboutin, and Felix J. Herrmann, “Normalizing flows for regularization of 3D seismic inverse problems”, ML4SEISMIC Partners Meeting. 2022.
M
Ting-ying Yu, Abhinav Prakash Gahlot, Rafael Orozco, Ziyi Yin, Mathias Louboutin, and Felix J. Herrmann, “Monitoring Subsurface CO2 Plumes with Sequential Bayesian Inference”, in International Meeting for Applied Geoscience and Energy, 2023.
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.
I
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.
Abhinav Prakash Gahlot, Huseyin Tuna Erdinc, Rafael Orozco, Ziyi Yin, and Felix J. Herrmann, “Inference of CO2 flow patterns – a feasibility study”, in Neural Information Processing Systems (NeurIPS), 2023.
E
Yunlin Zeng, Rafael Orozco, Ziyi Yin, and Felix J. Herrmann, “Enhancing Full-Waveform Variational Inference through Stochastic Resampling and Data Augmentation”, in International Meeting for Applied Geoscience and Energy, 2024.
D
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.
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, Haoyun Li, Ziyi Yin, Rafael Orozco, and Felix J. Herrmann, “A Digital Twin for Geological Carbon Storage with Controlled Injectivity”, in International Meeting for Applied Geoscience and Energy, 2024.
Ali Siahkoohi, “Deep generative models for solving geophysical inverse problems”, Georgia Institute of Technology, Atlanta, 2022.
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A. Witte, and Felix J. Herrmann, “Deep Bayesian Inference for Task-based Seismic Imaging”, in KAUST, 2021.
B
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”, in International Meeting for Applied Geoscience and Energy, 2024.
A
Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, and Felix J. Herrmann, “ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems”. 2024.
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, Ali Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann, “Adjoint operators as summary functions in amortized Bayesian inference frameworks”, ML4SEISMIC Partners Meeting. 2022.