{"693673":{"#nid":"693673","#data":{"type":"event","title":"PhD Defense by Abhinav P Gahlot","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EThesis title:\u003C\/strong\u003E Uncertainty-Aware Digital Twins for Subsurface Energy Systems: From Multimodal Bayesian Inference to Context-Aware Decision Making\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EReliable geological carbon storage requires estimates of subsurface CO\u2082 saturation and pressure despite incomplete observations and uncertain reservoir properties. This dissertation develops uncertainty-aware Digital Shadows and Digital Twins that combine physics-based simulation with amortized Bayesian inference for monitoring evolving reservoir states.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThe proposed framework uses ensembles propagated through multiphase flow, rock physics, seismic modeling, and imaging to train conditional normalizing flows that generate posterior samples from time-lapse seismic and well observations. The dissertation examines three extensions of this framework: multimodal monitoring, robustness to uncertain rock physics, and context-aware inference. Rock-physics augmentation improves reconstruction under tested model mismatch, while a three-phase extension enables joint estimation of oil and CO\u2082 saturation. A sensitivity-aware Digital Twin treats rock-physics assumptions as explicit context, supporting assumption-specific inference and what-if analysis. Finally, a collaborative case study demonstrates how probabilistic Digital Shadow estimates can be supplied to a separately developed injection-control workflow. Together, these studies provide a simulation-based framework for uncertainty-aware subsurface monitoring and decision support.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDefense date:\u003C\/strong\u003E November 6, 2026\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDefense time:\u003C\/strong\u003E 10:30 am - 01:30 pm EST\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDefense location:\u003C\/strong\u003E ES\u0026amp;T L1114\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAdvisor (Chair):\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EFelix Herrmann, Professor, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee members:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EAndrew Newman, Professor, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EZhigang Peng, Professor, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003ESheng Dai, Professor, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EOlav Moyner, Senior Research Scientist, SINTEF, Norway\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EUncertainty-Aware Digital Twins for Subsurface Energy Systems: From Multimodal Bayesian Inference to Context-Aware Decision Making\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Uncertainty-Aware Digital Twins for Subsurface Energy Systems: From Multimodal Bayesian Inference to Context-Aware Decision Making"}],"uid":"27707","created_gmt":"2026-10-30 18:31:48","changed_gmt":"2026-10-30 18:31:48","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-06T10:30:33-05:00","event_time_end":"2026-11-06T13:30:33-05:00","event_time_end_last":"2026-11-06T13:30:33-05:00","gmt_time_start":"2026-11-06 15:30:33","gmt_time_end":"2026-11-06 18:30:33","gmt_time_end_last":"2026-11-06 18:30:33","rrule":null,"timezone":"America\/New_York"},"location":"ES\u0026T L1114","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}