@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 {herrmann2023SIAMCSEtns,
	title = {The Next Step: Interoperable Domain-Specific Programming},
	booktitle = {SIAM Conference on Computational Science and Engineering},
	year = {2023},
	note = {(SIAM CSE, Amsterdam)},
	month = {02},
	abstract = {Even though domain-specific programming approaches allow for readable, scalable, and maintainable software without sacrificing performance, the new paradigm of learned physics-informed models calls for an interdisciplinary approach typically involving multiple domain-specific languages. Take for example the problem of inverting for the fluid-flow properties from time-lapse seismic data, which entails domain-specific programming on the intersection of wave simulators, matrix-free linear algebra, learned neural surrogates for two-phase flow, and prior and posterior distributions for the fluid-flow properties. While domain-specific solutions exist for each of these sub-disciplines, integrating these approaches {\textendash} which may involve different programming languages {\textendash} into a single coupled scalable inversion framework that supports algorithmic differentiation can be a challenge. However, we show that challenges like this can be met when working with proper abstractions. In our inversion example, this involves math-inspired symbolic abstractions for numerical solutions of the wave equation (Devito), matrix-free implementations for its Jacobians (JUDI.jl), abstractions for Automatic Differentiation (ChainRules.jl), and homegrown implementations for conditional Invertible Neural Networks (InvertibleNetworks.jl) and Fourier Neural Operators (ParametricOperators.jl).},
	keywords = {algorithms, deep learning, devito, end-to-end, Fourier neural operators, GCS, JUDI, Jutul, SIAM, software, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMCSE/2023/herrmann2023SIAMCSEtns/index.html},
	author = {Felix J. Herrmann and Mathias Louboutin and Thomas J. Grady II and Ziyi Yin and Rishi Khan}
}
