{ "149342-21906254": { "datetime_modified":"20260908T142951", "datetime_start":"20260918T150000", "datetime_end":"20260918T160000", "has_end_time":1, "date_start":"2026-09-18", "date_end":"2026-09-18", "time_start":"15:00:00", "time_end":"16:00:00", "time_zone":"America\/Detroit", "event_title":"Extremal set theory as algebraic geometry", "occurrence_title":"", "combined_title":"Extremal set theory as algebraic geometry: Russ Woodroofe (University of Primorska)", "event_subtitle":"Russ Woodroofe (University of Primorska)", "event_type":"Workshop \/ Seminar", "event_type_id":"21", "description":"Extremal set theory asks questions such as \"what is the largest family of pairwise intersecting k-element subsets of an n-element set?\" Many questions of this type can be modeled with exterior algebras, and admit extensions such as \"what is the largest self-annihilating subspace of k-forms in an n-variable exterior algebra?\" The shifting technique in extremal set theory corresponds to algebraic geometric limits of certain curves in a Grassmannian.\n\nIn this talk, I will overview these connections, and go on to discuss recent joint work with Bulavka and Gandini. We find a maximum subspace of k-forms in an n-variable exterior algebra that is self-annihilating but not annihilated by any 1-form. A difficulty is that \"not annihilated by any 1-form\" is not a closed condition in the Grassmannian. This work extends the Hilton-Milner theorem of extremal set theory to the exterior algebra setting, and answers questions of Scott and Wilmer and of my own.", "occurrence_notes":null, "guid":"149342-21906254@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/149342", "building_id":"1000166", "building_name":"East Hall", "campus_maps_id":"53", "room":"4088", "location_name":"East Hall", "has_livestream":0, "cost":"", "tags":["Mathematics"], "website":"", "sponsors":[ { "group_name":"Combinatorics Seminar - Department of Mathematics", "group_id":"4885", "website":"" }, { "group_name":"Department of Mathematics", "group_id":"3791", "website":"" } ], "image_url":"", "styled_images":{ "event_thumb":"", "event_large":"", "event_large_2x":"", "event_large_lightbox":"", "group_thumb":"", "group_thumb_square":"", "group_large":"", "group_large_lightbox":"", "event_large_crop":"", "event_list":"", "event_list_2x":"", "event_grid":"", "event_grid_2x":"", "event_feature_large":"", "event_feature_thumb":"" }, "occurrence_count":1, "first_occurrence":21906254 } , "148874-21905036": { "datetime_modified":"20260810T205656", "datetime_start":"20260918T150000", "datetime_end":"20260918T160000", "has_end_time":1, "date_start":"2026-09-18", "date_end":"2026-09-18", "time_start":"15:00:00", "time_end":"16:00:00", "time_zone":"America\/Detroit", "event_title":"Foundation models and agentic AI for discovery in fluid mechanics", "occurrence_title":"", "combined_title":"Foundation models and agentic AI for discovery in fluid mechanics: Ricardo Vinuesa (Aerospace Engineering, University of Michigan)", "event_subtitle":"Ricardo Vinuesa (Aerospace Engineering, University of Michigan)", "event_type":"Lecture \/ Discussion", "event_type_id":"13", "description":"Abstract: In this seminar we discuss a unified framework that combines explainable deep learning, deep reinforcement learning (DRL) and foundation models to advance both understanding and control of turbulence, with direct implications for accelerated design and discovery. First, we will show how explainable deep learning techniques can be used to identify the flow features that are truly responsible for key turbulent processes in wall-bounded flows. By systematically interrogating trained neural networks, we uncover the most influential coherent structures driving momentum transport and drag. Our results reveal that classically studied structures (while important) provide only a partial and sometimes misleading perspective, motivating a more data-driven and physics-aware view of turbulence organization. Building on these insights, we will demonstrate how deep reinforcement learning can be used to actively control turbulent flows by targeting the dynamically relevant structures identified through explainability. This approach achieves over 30% drag reduction in canonical wall-bounded turbulence and extends naturally to more complex configurations, including turbulent wings, highlighting the scalability of learning-based control strategies. Finally, we will introduce a foundation-model-based framework for accelerated design, optimization and scientific discovery. By learning compact, interpretable latent representations of high-dimensional flow physics, these models (combined with agentic-AI systems) enable rapid exploration of design spaces, causal reasoning and closed-loop optimization, bridging the gap between expensive simulations, control and engineering decision making. Together, these results illustrate how explainable and agentic AI are becoming essential for turbulence physics, flow control and next-generation engineering design.\n\n\nContact: Silas Alben", "occurrence_notes":null, "guid":"148874-21905036@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/148874", "building_id":"1000166", "building_name":"East Hall", "campus_maps_id":"53", "room":"1084", "location_name":"East Hall", "has_livestream":0, "cost":"", "tags":["Applied Mathematics","Mathematics"], "website":"", "sponsors":[ { "group_name":"Applied Interdisciplinary Mathematics (AIM) Seminar - Department of Mathematics", "group_id":"4882", "website":"" }, { "group_name":"Department of Mathematics", "group_id":"3791", "website":"" } ], "image_url":"", "styled_images":{ "event_thumb":"", "event_large":"", "event_large_2x":"", "event_large_lightbox":"", "group_thumb":"", "group_thumb_square":"", "group_large":"", "group_large_lightbox":"", "event_large_crop":"", "event_list":"", "event_list_2x":"", "event_grid":"", "event_grid_2x":"", "event_feature_large":"", "event_feature_thumb":"" }, "occurrence_count":1, "first_occurrence":21905036 } }