GT MAP is a place for research discussion and collaboration. We welcome participation of any researcher interested in discussing his/her project and exchange ideas with Mathematicians. Mathematics can provide useful tools and insights to different research projects, and GT-MAP is to provide a channel of various discussions.
Feel free to consider giving presentations in our seminar series and workshop.
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If you are interested, here is a link to School of Mathematics weekly Seminars and Colloquia.
GT-MAP Seminar Sep 20th , 2024(Friday) 3PM
Location: Skiles 006
Speaker: Prof. Felix Hermann
Georgia Research Alliance Eminent Scholar Chair in Energy
Seismic Laboratory for Imaging and Modeling
Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, Electrical and Computer Engineering
Georgia Institute of Technology
https://slim.gatech.edu
Title: Digital Twins in the era of generative AI — Application to Geological CO2 Storage
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 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.
This is joint work with Rafael Orozco.
Bio:
Felix J. Herrmann is a professor with appointments at the College of Sciences (EAS), Computing (CSE), and Engineering (ECE) at the Georgia Institute of Technology. He leads the Seismic Laboratory for Imaging and modeling (SLIM) and he is co-founder/director of the Center for Machine Learning for Seismic (ML4Seismic). This Center is designed to foster industrial research partnerships and drive innovations in artificial-intelligence assisted seismic imaging, interpretation, analysis, and time-lapse monitoring. In 2019, he toured the world presenting the SEG Distinguished Lecture. In 2020, he was the recipient of the SEG Reginald Fessenden Award for his contributions to seismic data acquisition with compressive sensing. Since his arrival at Georgia Tech in 2017, he expanded his research program to include machine learning for Bayesian wave-equation based inference using techniques from simulation-based inference. More recently, he started a research program on seismic monitoring of Geological Carbon Storage, which includes the development of an uncertainty-aware Digital Twin. In 2023, the manuscript entitled “Learned multiphysics inversion with differentiable programming and machine learning” was the most downloaded paper of 2023 in Society of Exploration Geophysicist’s The Leading Edge.
GT-MAP Seminar Nov 1st , 2024(Friday) 3PM
TBA
GT-MAP Seminar April 26th, 2024 (Friday) 3PM
Speaker: Prof. Yao Xie – Industrial & Systems Engineering (ISyE) at Georgia Tech
Location: Skiles 005
Title: Computing High-Dimensional Optimal Transport by Flow Neural Networks
Abstract: Flow-based models are widely used in generative tasks, including normalizing flow, where a neural network transports from a data distribution P to a normal distribution. This work develops a flow-based model that transports from P to an arbitrary Q (which can be pre-determined or induced as the solution to an optimization problem), where both distributions are only accessible via finite samples. We propose to learn the dynamic optimal transport between P and Q by training a flow neural network. The model is trained to optimally find an invertible transport map between P and Q by minimizing the transport cost. The trained optimal transport flow subsequently allows for performing many downstream tasks, including infinitesimal density ratio estimation (DRE) and distribution interpolation in the latent space for generative models. The effectiveness of the proposed model on high-dimensional data is demonstrated by strong empirical performance on high-dimensional DRE, OT baselines, and image-to-image translation.