{"688524":{"#nid":"688524","#data":{"type":"event","title":"PhD Proposal by Anushka S. Moharir","body":[{"value":"\u003Cp\u003EStudent Name: Anushka S. Moharir\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAdvisor: Dr. Dimitri Mavris\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EMilestone: PhD Thesis Proposal\u003Cbr\u003E\u003Cbr\u003EDegree Program: Aerospace Engineering\u003Cbr\u003E\u003Cbr\u003ETitle: Adaptive Aircraft Trajectory Optimization Under Environmental and Operational Uncertainty\u003Cbr\u003E\u003Cbr\u003EAbstract: Aircraft arrival operations in high-density terminal airspace are subject to significant environmental and operational uncertainty, including wind variability, traffic sequencing constraints, and air traffic control interventions. While modern descent procedures are optimized to reduce fuel burn and community noise exposure, they are typically designed under nominal conditions and implemented in an open-loop manner. In practice, deviations from assumed conditions degrade performance and reduce robustness. These limitations motivate the need for adaptive trajectory optimization frameworks capable of responding dynamically to evolving uncertainty. Reinforcement learning has emerged as a promising approach for adaptive trajectory generation due to its closed-loop decision structure. However, in safety-critical aerospace systems, RL-based methods face structural challenges related to robustness guarantees, interpretability, and constraint enforcement within physics-based simulation environments. Deterministic optimal control methods provide physical consistency but lack adaptivity, while surrogate modeling improves computational efficiency yet is often weakly integrated into adaptive control architectures. A unified framework that combines robustness, interpretability, and closed-loop adaptivity remains lacking. This thesis proposes a physics-grounded modeling and simulation framework for adaptive aircraft trajectory optimization under environmental and operational uncertainty. The research investigates three structural hypotheses: (1) uncertainty-aware deterministic formulations improve robustness without significant nominal performance degradation; (2) physics-informed surrogate models enable efficient adaptive re-computation under dynamic constraints; and (3) reinforcement learning with physics-grounded reward structures and explicit constraint enforcement achieves interpretable and stable closed-loop trajectory control. A reduced-order longitudinal flight dynamics model and controlled uncertainty scenarios are used to evaluate performance stability under perturbation and adaptive effectiveness. By reframing arrival trajectory optimization as a structured, uncertainty-aware feedback problem grounded in physical modeling, this work advances scalable methodologies for robust adaptive control in next-generation aviation systems.\u003Cbr\u003E\u003Cbr\u003EDate and time: 2026-03-27, 1 pm - 4 pm\u003Cbr\u003E\u003Cbr\u003ELocation: Collaborative Design Environment (CoVE), Weber (SST II)\u003Cbr\u003E\u003Cbr\u003ECommittee:\u003Cbr\u003EDr. Dimitri Mavris (advisor), School of Aerospace Engineering\u003Cbr\u003EProf. Dimitri Mavris, School of Aerospace Engineering\u003Cbr\u003EProf. Daniel Schrage, School of Aerospace Engineering\u003Cbr\u003EProf. Kai James, School of Aerospace Engineering\u003Cbr\u003EDr. Ameya Behere, School of Aerospace Engineering\u003Cbr\u003EDr. Angela Campbell, FAA\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAdaptive Aircraft Trajectory Optimization Under Environmental and Operational Uncertainty\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Adaptive Aircraft Trajectory Optimization Under Environmental and Operational Uncertainty"}],"uid":"27707","created_gmt":"2026-02-25 19:31:05","changed_gmt":"2026-02-25 19:31:41","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-27T13:00:00-04:00","event_time_end":"2026-03-27T16:00:00-04:00","event_time_end_last":"2026-03-27T16:00:00-04:00","gmt_time_start":"2026-03-27 17:00:00","gmt_time_end":"2026-03-27 20:00:00","gmt_time_end_last":"2026-03-27 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Collaborative Design Environment (CoVE), Weber (SST II)","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"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":""}}}