@unpublished {park2026IMAGEnoa,
	title = {Neural Operators with Accurate Jacobian for High-Fidelity Image-Domain Seismic Inversion},
	year = {2026},
	month = {3},
	abstract = {Neural operators (NOs) provide efficient surrogates for wave-equation-based imaging operators, but accurate forward prediction alone does not guarantee reliable inversion. As inversion updates depend on the adjoint Jacobian, errors in learned sensitivities lead to unphysical updates and degraded quality in recovered models. To this end, we propose a Jacobian-informed training strategy that enforces accuracy in Jacobian in the directions where gradient descent occurs during Least-Squares. Numerical experiments with Fourier Neural Operators demonstrate that supervising only a small fraction ($0.1 \sim 0.01\%$) of Jacobian information significantly improves gradient quality and leads to more accurate subsurface velocity reconstruction. In particular, the proposed approach reduces mid to deep layer artifacts and improves structural continuity compared to models trained only with forward misfit term. These results highlight that learning inversion-relevant sensitivities, rather than forward mappings alone, is critical for reliable surrogate-assisted seismic inversion.},
	keywords = {deep learning, derivative-informed, Fisher-information, FNO, IMAGE, Imaging, Inverse problems, reduced-order modeling, RTM, SEG, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/park2026IMAGEnoa/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/park2026IMAGEnoa/IMAGE_Jayjay.pdf},
	author = {Jeongjin Park and Huseyin Tuna Erdinc and Grant Bruer and Richard Rex and Nisha Chandramoorthy and Felix J. Herrmann}
}
@conference {park2025ML4SEISMICftn,
	title = {Fisher-Informed Training of Neural Operators for Reliable PDE Inversion},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Neural operators have shown strong performance for PDE-solution learning; however, their effectiveness in PDE-constrained optimization, such as inversion, remains limited, in part because inaccurately learned derivatives can mislead gradient-based updates. Standard training typically optimizes only the forward least-squares fit, so during iterative inversion the optimization variable can drift outside the training distribution, further degrading quality of derivative information and inversion performance. To address this limitation, we propose a training algorithm designed for inversion: the surrogate is trained not only to predict forward outputs but also to learn observation-relevant gradients, which leads to Fisher-information directed parameter updates. We evaluate forward and gradient accuracy, as well as the inversion trajectory, on problems governed by PDEs{\textemdash}Darcy flow and laminar (incompressible) Navier{\textendash}Stokes{\textemdash}which are relevant to porous-media fluid flow and energy-storage applications. Our results suggest that training for gradient fidelity with forward accuracy unlocks a pathway to reliable, efficient, neural operator{\textendash}based inversion.},
	keywords = {deep learning, derivatve-informed, Fisher-information, FNO, GCS, Inverse problems, ML4SEISMIC, permeability, reduced-order modeling, reservoir simulation, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/park2025ML4SEISMICftn},
	author = {Jeongjin Park and Grant Bruer and Huseyin Tuna Erdinc and Richard Rex Arockiasamy and Nisha Chandramoorthy and Felix J. Herrmann}
}
@conference {park2025IMAGErdn,
	title = {A reduced-order derivative-informed neural operator for subsurface fluid-flow},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {deep learning, derivatve-informed, Fisher-information, FNO, GCS, IMAGE, Inverse problems, permeability, reduced-order modeling, reservoir simulation, SEG, Uncertainty quantification},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/park2025IMAGErdn/IMAGE_Jayjay.pdf},
	author = {Jeongjin Park and Grant Bruer and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {herrmann2024SIAMUQdt,
	title = {An Uncertainty-Aware Digital Twin for Geological Carbon Storage},
	booktitle = {SIAM Conference on Uncertainty Quantification},
	year = {2024},
	note = {(SIAM UQ, Trieste)},
	month = {03},
	abstract = {Arguably, Geological Carbon Storage constitutes the only truly scalable net-negative carbon emission technology. To mitigate its risks and optimize its operations, an uncertainty-aware Digital Twin is being developed. To leverage existing fluid-flow and seismic simulation and imaging capabilities, the envisioned twin combines techniques from sequential and simulation-based Bayesian inference to train its deep generative neural networks to draw samples from the posterior of the Digital Twin{\textquoteright}s state. Because these samples are conditioned on observed time-lapse field data, these twins are capable of capturing the dynamics of CO2 plumes and their uncertainty.},
	keywords = {Bayesian inference, CCS, deep learning, digital twin, GCS, Imaging, monitoring, SIAM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMUQ/2024/digital-twin},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Grant Bruer and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
