@unpublished {orozco2023invnet,
	title = {InvertibleNetworks.jl: A Julia package for scalable normalizing flows},
	year = {2023},
	month = {12},
	abstract = {InvertibleNetworks.jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributions. This package excels in memory efficiency by leveraging the inherent invertibility of normalizing flows, which significantly reduces memory requirements during backpropagation compared to existing normalizing flow packages that rely on automatic differentiation frameworks. InvertibleNetworks.jl has been adapted for diverse applications, including seismic imaging, medical imaging, and CO2 monitoring, demonstrating its effectiveness in learning high-dimensional distributions.},
	keywords = {Bayesian inference, computing, conditional normalizing flows, deep learning, HPC, Inverse problems, memory, Normalizing flows, software, Uncertainty quantification},
	doi = {10.48550/arXiv.2312.13480},
	author = {Rafael Orozco and Philipp A. Witte and Mathias Louboutin and Ali Siahkoohi and Gabrio Rizzuti and Bas Peters and Felix J. Herrmann}
}
@article {louboutin2023lmi,
	title = {Learned multiphysics inversion with differentiable programming and machine learning},
	journal = {The Leading Edge},
	volume = {42},
	year = {2023},
	note = {(The Leading Edge)},
	month = {07},
	pages = {452-516},
	abstract = {We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, our software is designed to be both readable and scalable. This allows researchers to easily formulate their problems in an abstract fashion while exploiting the latest developments in high-performance computing. We illustrate and demonstrate our design principles and their benefits by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which aside from coupling of wave physics and multiphase flow, involves machine learning.},
	keywords = {CCS, deep learning, Fourier neural operators, GCS, Imaging, inversion, invertible networks, machine learning, monitoring, multiphysics, Normalizing flows, SLIM, software, time-lapse},
	url = {https://library.seg.org/doi/10.1190/tle42070474.1},
	presentation = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.html},
	author = {Mathias Louboutin and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Ali Siahkoohi and Gabrio Rizzuti and Philipp A. Witte and Olav M{\o}yner and Gerard J. Gorman and Felix J. Herrmann}
}
@conference {louboutin2022RHPCafa,
	title = {Abstractions for at-scale seismic inversion},
	booktitle = {Rice Oil and Gas High Performance Computing Conference 2022},
	year = {2022},
	note = {Rice Oil and Gas High Performance Computing Conference 2022},
	month = {03},
	pages = {Thursday Workshop: Devito Training and Hackathon},
	abstract = {We present the SLIM open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g., medical ultrasound). We developed a software environment aimed at scalable research and development by designing multiple layers of abstractions. This environment allows the researchers to easily formulate their problem in an abstract fashion, while still being able to exploit the latest developments in high-performance computing. We illustrate and demonstrate the benefits of our software design on many geophysical applications, including seismic inversion and physics-informed machine learning for geophysics (e.g., loop unrolled imaging, uncertainty quantification), all while facilitating the integration of external software.},
	keywords = {CCS, devito, FWI, HPC, inversion, JUDI, machine learning, RHPC, software, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/RHPC/2022/louboutin2022RHPCafa/RiceHPC22.pdf},
	url2 = {https://youtu.be/scRTbP8w6Wk?t=4542},
	author = {Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Yijun Zhang and Philipp A. Witte and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {louboutin2022SEGais,
	title = {Accelerating innovation with software abstractions for scalable computational geophysics},
	booktitle = {International Meeting for Applied Geoscience and Energy Expanded Abstracts},
	year = {2022},
	note = {(IMAGE, Houston)},
	month = {05},
	abstract = {We present the SLIM open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g., medical ultrasound). We developed a software environment aimed at scalable research and development by designing multiple layers of abstractions. This environment allows the researchers to easily formulate their problem in an abstract fashion, while still being able to exploit the latest developments in high-performance computing. We illustrate and demonstrate the benefits of our software design on many geophysical applications, including seismic inversion and physics-informed machine learning for geophysics(e.g., loop unrolled imaging, uncertainty quantification), all while facilitating the integration of external software.},
	keywords = {CCS, devito, FWI, HPC, inversion, JUDI, machine learning, SEG, software, Uncertainty quantification},
	doi = {10.1190/image2022-3750561.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/louboutin2022SEGais/louboutin_seg22.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/louboutin2022SEGais/index.html},
	author = {Mathias Louboutin and Philipp A. Witte and Ali Siahkoohi and Gabrio Rizzuti and Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
@presentation {yin2022TRANSFORMjulia,
	title = {Julia for Geoscience},
	year = {2022},
	month = {04},
	abstract = {In this tutorial, we will introduce the Julia programming language to the geoscience community, covering topics such as I/O, data processing, inversion, and machine learning. We will begin by installing Julia and relevant packages. Through a series of tutorials, we will demonstrate Julia{\textquoteright}s abstraction power and show how to load and plot data, write your own functions/operators, form and solve a geophysical inverse problem, and demonstrate how to integrate wave-equation solvers in Julia with machine learning frameworks. The intent of this presentation is to provide an introductory level tutorial that will be useful to members of the geoscience community.},
	keywords = {JOLI, JUDI, Julia, machine learning, SLIM, software, TRANSFORM},
	url = {https://transform.softwareunderground.org/2022-julia-for-geoscience},
	url2 = {https://www.youtube.com/watch?v=HyWfp3NzIbg},
	software = {https://github.com/slimgroup/SLIMTutorials},
	author = {Ziyi Yin and Mathias Louboutin and Philipp A. Witte and Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Henryk Modzelewski and Felix J. Herrmann}
}
