Digital Twins in the era of generative AI — Application to Geological CO2 Storage
| Title | Digital Twins in the era of generative AI — Application to Geological CO2 Storage |
| Publication Type | Conference |
| Year of Publication | 2024 |
| Authors | Abhinav Prakash Gahlot, Rafael Orozco, Haoyun Li, Huseyin Tuna Erdinc, Ziyi Yin, Mathias Louboutin, Felix J. Herrmann |
| Conference Name | ICON Seminar in IoT |
| Month | 9 |
| Keywords | Bayesian inference, conditional normalizing flows, differential programming, digital twin, Ensemble Bayesian filtering, GCS, multiphyics, sequential Bayes, Uncertainty quantification |
| Abstract | As a society, we are faced with important challenges to combat climate change. Geological Carbon Storage, during which gigatonnes of super-critical CO2 are stored underground, is arguably the only scalable net-negative CO2-emission technology that is available. Recent advances in generative AI offer unique opportunities—especially in the context of Digital Twins for subsurface CO2-storage monitoring, decision making, and control—to help scale this technology, optimize its operations, lower its costs, and reduce its risks, so assurances can be made whether storage projects proceed as expected and whether CO2 remains underground. During this talk, it is shown how techniques from Simulation-Based Inference and Ensemble Bayesian Filtering can be extended to establish probabilistic baselines and assimilate multimodal data for problems challenged by large degrees of freedom, nonlinear multiphysics, and computationally expensive to evaluate simulations. Key concepts that will be reviewed include neural Wave-Based Inference with Amortized Uncertainty Quantification and physics-based Summary Statistics, Ensemble Bayesian Filtering with Conditional Neural Networks, and learned multiphysics inversion with Differentiable Programming. |
| URL | https://slim.gatech.edu/Publications/Public/Conferences/PURDUEicon/2024/herrmann2024PURDUEicon |
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| Citation Key | herrmann2024PURDUEicon |