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X-WR-CALDESC:FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship
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DTSTART;TZID=Europe/Berlin:20231122T140000
DTEND;TZID=Europe/Berlin:20231122T150000
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SUMMARY:FAU MoD Lecture Series. Special November 2023
DESCRIPTION:Date: Wed. November 22, 2023\nEvent: FAU MoD Lecture Series (Special double session. November 2023)\nOrganized by: FAU MoD, the FAU Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nFirst session: 13:45H\nFAU MoD Lecture: Prospects of formalized mathematics\nSpeaker: Prof. Dr. Michael Kohlhase\nAffiliation: Friedrich-Alexander-Universität Erlangen-Nürnberg. Carnegie Mellon University\nAbstract. In (informal) mathematics (pure and applied) a human studies rigorously represented objects or mathematical models of the real world, comes up with conjectures about their properties, proves or refutes them, submits them for review and finally publication in the academic literature. \nWhile it is commonly accepted that all of mathematics could be expressed and indeed developed in first-order logic based on (some axiomatic) set theory, this option is almost never executed in practice.\nFormalized mathematics aims to enable computer support of “doing mathematics” by representing objects, conjectures, proofs, and even publications in formal systems, usually expressive logical languages with machine-checkable proof calculi, and highly efficient algorithms for automating various aspects of “doing mathematics”.\nHighlights of formalized mathematics are\n– machine-checked proofs of major theorems like the Kepler Conjecture,\n– Feit/Thomson’s “odd order theorem”, or the four color theorem,\n– search engines for mathematical formulae,\n– synthesis and verification of computer algebra algorithms\n– multiple libraries of formalized and verified mathematics\n with more than 100.000 theorems/proofs each. \nThis talk will give an overview over the issues and results and introduces some of the techniques.\nSecond session: 15:00H\nFAU MoD Lecture: Rigorous analysis and numerical implementation of nudging data assimilation algorithms\nSpeaker: Prof. Dr. Edriss S. Titi\nAffiliation: University of Cambridge. Texas A&M University. Weizmann Institute of Science\nAbstract. In this talk, I will introduce downscaling data assimilation algorithms for weather and climate prediction based on discrete coarse spatial scale measurements of the state variables (or only part of them, depending on the underlying model). The algorithm is based on linear nudging of the coarse spatial scales in the algorithm’s solution toward the observed measurements of the coarse spatial scales of the unknown reference solution. The algorithm’s solution can be initialized arbitrarily and is shown to converge at an exponential rate toward the exact unknown reference solution. This indicates that the dynamics of the algorithm is globally stable (not chaotic) unlike the dynamics of the model that governs the unknown reference solution. Capitalizing on this fact, I will also demonstrate uniform in time error estimates of the numerical discretization of these algorithms, which makes them reliable upon implementation computationally. Furthermore, I will also present a recent improvement of this algorithm by employing nonlinear nudging, which yields a super exponential convergence rate toward the unknown exact reference solution.\nWHEN\nWed. November 22, 2023\n13:45H to 16:15H (Berlin time)\nWHERE\nOn-site / Online\n[On-site]\nRoom H16 Hörsaal\nGPS-Koord. Raum: 49.573365N, 11.028918E\nFriedrich-Alexander-Universität Erlangen-Nürnberg.\nCauerstraße 7-9, 91058 Erlangen (Germany)\n[Online]\nFAU Zoom link\nMeeting ID: 624 1094 3213 | 694096\nOur speakers\nMichael Kohlhase member at our FAU MoD is professor for Knowledge Representation/Processing (Computer Science) at Friedrich-Alexander-Universität Erlangen-Nürnberg and adjunct associate professor for Computer Science at Carnegie Mellon University.\nHis research interests include knowledge representation for STEM (Science, Technology, Engineering, Mathematics), inference-based techniques for natural language processing, computer-supported education and user assitance. He pursues these (interrelated) topics focusing on the aspects of modular foundations (usually logical methods) and large-scale structures in document corpora. The research is conducted in the context of the KWARC group (Knowledge Adaptation and Reasoning for Content) and in extended visits to Carnegie Mellon University, SRI International, and the Universities of Amsterdam, Edinburgh, and Auckland. \nEdriss S. Titi received his doctorate in 1986 under the supervision of Ciprian Foias. Currently he holds the Nonlinear Mathematical Sciences Professorial Chair at the University of Cambridge, UK; he is a University Distinguished Professor and the Arthur Owen Professor of Mathematics in Texas A&M University; moreover, of Computer Science and Applied Mathematics at the Weizmann Institute of Science in Israel. \nTiti’s research in applied and computational mathematics lies at the interface between rigorous applied analysis and physical applications. Specifically, in studying the Euler and the Navier-Stokes and other related nonlinear partial differential equations.\nTiti is a Fellow of the American Mathematical Society, the Society of Industrial and Applied Mathematics, the John Simon Guggenheim Memorial Foundation, USA; and the Institute of Physics, UK. He is also the recipient of many international scientific awards including the Humboldt Research Award and the Einstein Visiting Fellow. He is also a co-recipient of the Society for Industrial and Applied Mathematics (SIAM) Prize on Best Paper in Partial Differential Equations (2009), and the 2020 International Consortium of Chinese Mathematicians Best Paper Award (Gold Medal).\nThis event on LinkedIn\n\nSee poster\n_\nYou might like:\n• FAU MoD Lectures\n• FAU MoD Lecture: The role of Artificial Intelligence in the future of mathematics by Prof. Dr. Amaury Hayat\n• FAU MoD Lecture: Free boundary regularity for the obstacle problem by Prof. Dr. Alessio Figalli\n• FAU MoD Lecture: Physics-Based and Data-Driven-Based Algorithms for the Simulation of the Heart Function by Prof. Dr. Alfio Quarteroni\n• FAU MoD Lecture: From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus? by Prof. Dr. George Karnidiakis\n• FAU MoD Lecture: From Alan Turing to contact geometry: Towards a “Fluid computer” by Prof. Dr. Eva Miranda\n• FAU MoD Lecture: Applications of AAA Rational Approximation by Prof. Dr. Nick Trefethen\n• FAU MoD Lecture: Learning-Based Optimization and PDE Control in User-Assignable Finite Time by Prof. Dr. Miroslav Krstic\n_\nDon’t miss out our last news and connect with us!\nFAU DCN-AvH\nX (Twitter) @UniFAU_DCN | LinkedIn | GitHub | Instagram @unifau_dcn | YouTube @faudcnavh\nFAU MoD\nLinkedIn | Twitter | Instagram\n
URL:https://dcn.nat.fau.eu/events/fau-mod-lecture-series-special-november-2023/
ORGANIZER;CN=FAU MoD:MAILTO:
CATEGORIES:FAU MoD Lecture,Seminar/Talk
LOCATION:Worldwide
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