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2019
Philipp A. Witte, Mathias Louboutin, Navjot Kukreja, Fabio Luporini, Michael Lange, Gerard J. Gorman, and Felix J. Herrmann, “A large-scale framework for symbolic implementations of seismic inversion algorithms in Julia”, Geophysics, vol. 84, pp. F57-F71, 2019.
Mengmeng Yang, Marie Graff, Rajiv Kumar, and Felix J. Herrmann, “Low-rank representation of subsurface extended image volumes with power iterations”, in SEG Technical Program Expanded Abstracts, 2019, pp. 4470-4474.
Rajiv Kumar, Marie Graff-Kray, Ivan Vasconcelos, and Felix J. Herrmann, “Target-oriented imaging using extended image volumes—a low-rank factorization approach”, Geophysical Prospecting, vol. 67, pp. 1312-1328, 2019.
2020
Fabio Luporini, Mathias Louboutin, Michael Lange, Navjot Kukreja, Philipp A. Witte, Jan Hückelheim, Charles Yount, Paul H. J. Kelly, Felix J. Herrmann, and Gerard J. Gorman, “Architecture and performance of Devito, a system for automated stencil computation”, ACM Trans. Math. Softw., vol. 46, 2020.
Mathias Louboutin, Fabio Luporini, Philipp A. Witte, Rhodri Nelson, George Bisbas, Thorbecke, J., Felix J. Herrmann, and Gerard Gorman, “Scaling through abstractions – high-performance vectorial wave simulations for seismic inversion with Devito”, Georgia Institute of Technology, TR-CSE-2020-3, 2020.
2021
Thomas J. Grady II, Rishi Khan, and Felix J. Herrmann, “Distributed Fourier Neural Operators”, ML4SEISMIC Partners Meeting. 2021.
Mengmeng Yang, Marie Graff, Rajiv Kumar, and Felix J. Herrmann, “Low-rank representation of omnidirectional subsurface extended image volumes”, Geophysics, vol. 86, p. 1MJ-WA152, 2021.
George Bisbas, Fabio Luporini, Mathias Louboutin, Rhodri Nelson, Gerard Gorman, and Paul H. J. Kelly, “Temporal blocking of finite-difference stencil operators with sparse "off-the-grid" sources”, in IEEE International Parallel and Distributed Processing Symposium, 2021.
2022
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.
Ali Siahkoohi, Thomas J. Grady II, Abhinav Prakash Gahlot, Huseyin Tuna Erdinc, and Felix J. Herrmann, “Capturing velocity-model uncertainty and two-phase flow with Fourier Neural Operators”, in EAGE Annual Conference Proceedings, 2022, p. AI in Geoscience and Geophysics: Current Trends and Future Prospects (Dedicated Session).
Ali Siahkoohi, Thomas J. Grady II, Abhinav Prakash Gahlot, Huseyin Tuna Erdinc, and Felix J. Herrmann, “Capturing velocity-model uncertainty and two-phase flow with Fourier Neural Operators”, in EAGE Annual Conference Proceedings, 2022, p. AI in Geoscience and Geophysics: Current Trends and Future Prospects (Dedicated Session).
Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Ziyi Yin, Mathias Louboutin, and Felix J. Herrmann, “De-risking Carbon Capture and Sequestration with Explainable CO$_2$ Leakage Detection in Time-lapse Seismic Monitoring Images”, in AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges, 2022.
Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Ziyi Yin, Mathias Louboutin, and Felix J. Herrmann, “De-risking GCS projects with explainable CO$_2$ leakage detection in time-lapse seismic images”, ML4SEISMIC Partners Meeting. 2022.
Thomas J. Grady II, Rishi Khan, Mathias Louboutin, Ziyi Yin, Philipp A. Witte, Ranveer Chandra, Russell J. Hewett, and Felix J. Herrmann, “Effective scaling of numerical surrogates via domain-decomposed Fourier neural operators”, ML4SEISMIC Partners Meeting. 2022.
Navjot Kukreja, Jan Hueckelheim, Mathias Louboutin, John Washbourne, Paul H. J. Kelly, and Gerard J. Gorman, “Lossy Checkpoint Compression in Full Waveform Inversion”, Geoscientific Model Development, vol. 15, pp. 3815-3829, 2022.
Mathias Louboutin, Ziyi Yin, Rafael Orozco, Thomas J. Grady II, and Felix J. Herrmann, “ML4Seismic open-source software: updates and developments”, ML4SEISMIC Partners Meeting. 2022.
Ziyi Yin, Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Mathias Louboutin, and Felix J. Herrmann, “Simulation-based framework for geological carbon storage monitoring”, ML4SEISMIC Partners Meeting. 2022.
2023
Ziyi Yin, Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Mathias Louboutin, and Felix J. Herrmann, “Derisking geologic carbon storage from high-resolution time-lapse seismic to explainable leakage detection”, The Leading Edge, vol. 42, pp. 69-76, 2023.
Felix J. Herrmann, Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Ziyi Yin, and Mathias Louboutin, “Derisking geological storage with simulation-based seismic monitoring design and machine learning”, in Carbon, Capture, Utilization, and Storage, 2023.
Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Mathias Louboutin, and Felix J. Herrmann, “Enhancing CO2 Leakage Detectability via Dataset Augmentation”, in International Meeting for Applied Geoscience and Energy, 2023.
Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Mathias Louboutin, and Felix J. Herrmann, “Improved automatic seismic CO2 leakage detection via dataset augmentation”, ML4SEISMIC Partners Meeting. 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.
Richard Rex, Thomas J. Grady II, Rishi Khan, Ziyi Yin, and Felix J. Herrmann, “Large-scale parametric PDE approximations with model-parallel Fourier neural operators”, ML4SEISMIC Partners Meeting. 2023.
Mathias Louboutin, Ziyi Yin, Rafael Orozco, Thomas J. Grady II, Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte, Olav Møyner, Gerard J. Gorman, and Felix J. Herrmann, “Learned multiphysics inversion with differentiable programming and machine learning”, The Leading Edge, vol. 42, pp. 452-516, 2023.
Mathias Louboutin, Ziyi Yin, Rafael Orozco, Thomas J. Grady II, Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte, Olav Møyner, Gerard J. Gorman, and Felix J. Herrmann, “Learned multiphysics inversion with differentiable programming and machine learning”, The Leading Edge, vol. 42, pp. 452-516, 2023.

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