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        "event_title":"Bloody Work: Lexington and Concord 1775",
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        "combined_title":"Bloody Work: Lexington and Concord 1775",
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        "description":"The William L. Clements Library is pleased to announce a forthcoming exhibition in recognition of the 250th Anniversary of the military hostilities that began the American Revolutionary War. The Battles of Lexington and Concord are firmly established in American memory as the culmination of a range of governmental, political, economic, and social tensions that amplified in the decade leading up to 1775. In this exhibit, visitors will have the opportunity to see original historical manuscript letters, documents, newspapers, and artwork that reveal aspects of the bloody work of Empire and individual alike in April 1775.\r\n\r\nAmong the items on display will be Commander in Chief of the British Army, General Thomas Gage's draft orders for the Concord Expedition, April 18, 1775; a bundle of letters collected by former Sons of Liberty supporter Dr. Benjamin Church, which he secretly turned over to British Army intelligence; letters by Silas Deane, John Hancock, and Rachel Revere; and much more.\r\n\r\nOpen weekdays from 12-4 pm.",
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        "tags":["Library","libraries","history","Free","Exhibition","Exhibit","Ann Arbor","Americana","american history","american culture"],
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        "datetime_modified":"20250407T223224",
        "datetime_start":"20250411T120000",
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        "event_title":"Frontiers in Scientific Machine Learning Seminar 12: Operator Networks Based on Numerical Analysis",
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        "combined_title":"Frontiers in Scientific Machine Learning Seminar 12: Operator Networks Based on Numerical Analysis: Youngjoon Hong (Seoul National University)",
        "event_subtitle":"Youngjoon Hong (Seoul National University)",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"11th April 2025, 12pm -1pm\r\nVenue: 2636 GGBA and Zoom \r\nZoom Link: https:\/\/umich.zoom.us\/j\/97823527756?pwd=H01BbvtuG5q02Wzb8LJvhUnvijlAIe.1\r\n\r\nAbstract: We propose a novel approach for solving parametric partial differential equations (PDEs) using an operator network. This method combines the strengths of deep learning with traditional numerical techniques, specifically the finite element or spectral element methods, to address parametric PDEs ranging from singularly perturbed convection-diffusion equations to the Navier-Stokes equations, all without requiring paired input-output training data. Through extensive experiments on various benchmark problems, our approach has demonstrated excellent performance across diverse settings, showcasing its versatility in terms of accuracy, generalization, and computational efficiency. The proposed framework holds significant potential for applications in fields where PDEs are critical for modeling complex domains with diverse boundary conditions and singular behavior. Additionally, we provide a rigorous theoretical convergence analysis, grounded in numerical analysis, to further support our approach.\r\n\r\nBio: As an Associate Professor at Seoul National University, Youngjoon Hong focuses his research on the mathematics of machine learning and its applications in scientific computing. He earned his Ph.D. in Mathematics with a minor in Scientific Computing from Indiana University under the guidance of Professor Roger Temam, where he developed a strong foundation in theoretical analysis and computational techniques. During his postdoctoral work at the University of Illinois at Chicago with Professors Jerry Bona and David Nicholls, he conducted research on electromagnetic and water waves, further honing his problem-solving skills. He later initiated independent studies on the use of neural networks in scientific research, leading to a focused research program at San Diego State University, where he began investigating neural network approximation within scientific machine learning. Currently, his work is dedicated to developing theoretical frameworks that enhance the reliability and applicability of neural networks in addressing complex scientific and engineering challenges.",
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        "tags":["Sciml","Scientific Computing","North Campus","Machine Learning","Deep Learning","Ai In Science And Engineering"],
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        "datetime_end":"20250411T133000",
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        "event_title":"Global Sip & Paint: Celebrating Heritage",
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        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"Reflect cultural heritage in a fun and unique way by painting while sipping and partaking in cultural drinks and snacks from around the world! A light lunch will also be served.\u00a0",
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