@presentation {yin2023CSEslim,
	title = {Introduction to Seismic Laboratory for Imaging and Modeling},
	year = {2023},
	note = {(CSE Student Recruiting Event)},
	month = {11},
	abstract = {We introduce the latest research developments in Seismic Laboratory for Imaging and Modeling (SLIM), including geological carbon storage monitoring, seismic and medical imaging and uncertainty quantification, and advancements in scientific machine learning and high performance computing.},
	keywords = {CCS, deep learning, Fourier neural operators, GCS, Inverse problems, learned constraints, learned surrogates, medical imaging, multiphysics, Normalizing flows, PDE, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSE/2023/SLIM.pdf},
	author = {Ziyi Yin}
}
@article {yin2023smi,
	title = {Solving multiphysics-based inverse problems with learned surrogates and constraints},
	journal = {Advanced Modeling and Simulation in Engineering Sciences},
	volume = {10},
	year = {2023},
	note = {(Advanced Modeling and Simulation in Engineering Sciences)},
	month = {10},
	abstract = {Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO2 plume predictions near, and far away, from the monitoring wells.},
	keywords = {AMSES, deep learning, Fourier neural operators, Inverse problems, learned constraints, learned surrogates, multiphysics, Normalizing flows},
	doi = {10.1186/s40323-023-00252-0},
	url = {https://doi.org/10.1186/s40323-023-00252-0},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/AMSES/2023/yin2023smi/paper.html},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {yin2023HOTCSEspi,
	title = {Solving PDE-based inverse problems with learned surrogates and constraints},
	year = {2023},
	note = {(HotCSE)},
	month = {11},
	abstract = {In this presentation, I will introduce a learned inversion algorithm for solving inverse problems with computationally expensive forward operators. We tackle this challenge by combining learned surrogates (Fourier neural operators) with learned constraints (normalizing flows). After jointly training these networks with the same samples, the learned surrogates lead to computationally efficient surrogate-assisted inversion. Meanwhile, the learned constraints safeguard the accuracy of the surrogates by forcing the model iterates to remain in-distribution. By combining the two, we come up with a homotopy / continuation scheme where the constraints are relaxed slowly so that the data misfit objective can be minimized while the model iterates always remain in the statistical distribution on which the surrogates are trained. We demonstrate the efficacy of our learned inversion algorithm through carefully selected experiments centered around the problem of geological carbon storage monitoring.},
	keywords = {deep learning, Fourier neural operators, Inverse problems, learned constraints, learned surrogates, multiphysics, Normalizing flows, PDE},
	url = {https://slim.gatech.edu/Publications/Public/Lectures/HotCSE/2023/yin2023HOTCSEspi},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
