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        "time_zone":"America\/Detroit",
        "event_title":"Title TBA",
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        "combined_title":"Title TBA: Andrey Mishchenko (Open AI)",
        "event_subtitle":"Andrey Mishchenko (Open AI)",
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        "description":"Abstract:  TBA\n\nContact:  Karen Smith",
        "occurrence_notes":"Note the special location!",
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        "datetime_modified":"20260810T205616",
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        "event_title":"Pseudo-magnetism in a strained discrete honeycomb lattice",
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        "combined_title":"Pseudo-magnetism in a strained discrete honeycomb lattice: Xuenan Li (Mathematics, Purdue University)",
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        "description":"Abstract: Slowly-varying nonuniform strain in honeycomb media can generate an effective pseudo-magnetic field, even though the underlying medium is non-magnetic. This phenomenon was first discovered in graphene and later in photonic crystals, and other physical settings. In this talk, I will start from a discrete nearest-neighbor tight-binding model for a nonuniformly strained honeycomb medium. I will then show how this model leads to a continuum magnetic Dirac Hamiltonian that describes wave packets concentrated near a Dirac point of the undeformed structure. I will focus on unidirectional deformations with bounded gradients that preserve translation invariance along the armchair direction. In this setting, we prove the existence of eigenmodes that behave like plane waves along the armchair direction and are strongly localized in the transverse direction, together with quantitative correction estimates. These results apply to deformations that produce an approximately constant pseudo-magnetic field perpendicular to the plane. They also lead to nearly flat bands and, as a consequence, a very high density of states. These analytical results are also supported by numerical simulations of the corresponding deformations. This is joint work with Michael I. Weinstein.\n\nContact:  Ian Tobasco",
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        "datetime_modified":"20260810T205656",
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        "event_title":"Foundation models and agentic AI for discovery in fluid mechanics",
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        "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)",
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        "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",
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        "combined_title":"Title TBA: Sabina Haque (Mathematics, University of Michigan)",
        "event_subtitle":"Sabina Haque (Mathematics, University of Michigan)",
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        "description":"Abstract:  TBA\n\nContact:  Zhiyan Ding",
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        "combined_title":"Title TBA: Mitchell Luskin (Mathematics, University of Minnesota)",
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        "event_title":"Open Slot",
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        "combined_title":"Reserved: Joshua B. Plotkin (University of Pennsylvania)",
        "event_subtitle":"Joshua B. Plotkin (University of Pennsylvania)",
        "event_type":"Lecture \/ Discussion",
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        "description":"Abstract:  TBA\n\nContact: Naoki Masuda",
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