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SUMMARY:FAU MoD Lecture: Data Driven Modeling for Scientific Discovery and Digital Twins
DESCRIPTION:Date: Mon. April 20, 2026\nEvent: FAU MoD Lecture\nOrganized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nFAU MoD Lecture: Data Driven Modeling for Scientific Discovery and Digital Twins\nSpeaker: Prof. Dr. Dongbin Xiu\nAffiliation: Department of Mathematics. The Ohio State University (USA)\nAbstract.  We present a data-driven modeling framework for scientific discovery, termed Flow Map Learning (FML). This framework enables the construction of accurate predictive models for complex systems that are not amenable to traditional modeling approaches. By leveraging data and the expressiveness of deep neural networks (DNNs), FML facilitates long-term system modeling and prediction even when governing equations are unavailable.\nFML is particularly powerful in the context of Digital Twins, an emerging concept in digital transformation. With sufficient offline learning, FML enables the construction of simulation models for key quantities of interest (QoIs) in complex Digital Twins, when direct mathematical modeling of the QoIs is infeasible. During the online execution of a Digital Twin, the learned FML model can simulate the QoIs without reverting to the computationally intensive Digital Twin simulation model. As a result, FML serves as an enabling methodology for real-time control and optimization for complex systems.\nOUR SPEAKER\nDongbin Xiu received his PhD in Applied Mathematics from Brown University in 2004. He joined the Department of Mathematics at Purdue University in 2005 and moved to the University of Utah in 2013. In 2016, he joined The Ohio State University as a Professor of Mathematics and an Ohio Eminent Scholar. Dr. Xiu received the NSF CAREER Award in 2007 and was elected a SIAM Fellow in 2023. He has served on editorial boards of many journals and is currently the Editor-inChief of the Journal of Computational Physics. Additionally, he is the founding Associate Editor-in-Chief of the International Journal for Uncertainty Quantification (IJUQ) and the founding Editor-in-Chief of the Journal of Machine Learning for Modeling and Computing (JMLMC). His current research focuses on developing efficient numerical methods for data-driven modeling, scientific machine learning, Digital Twins, and uncertainty quantification.\n\nSee poster\nAUDIENCE\nThis is a hybrid event (On-site/online) open to: Public, Students, Postdocs, Professors, Faculty, Alumni and the scientific community all around the world.\nWHEN\nMon. April 20, 2026 at 11:00H (Berlin time)\nWHERE\nOn-site / Online\n[On-site]\n[On-site] Friedrich-Alexander-Universität Erlangen-Nürnberg.\nRoom H13. Johann-Radon-Hörsaal\nCauerstraße 11, 91058 Erlangen\nGPS-Koord. Raum: 49.573764N, 11.030028E\n[Online]\nhttps://www.fau.tv/clip/id/63064\n \nShortlink to share this event: https://go.fau.de/1dhaf\nThis event @LinkedIn\n \nYou might like:\n• FAU MoD Lectures\n• Upcoming events\n• FAU MoD Courses & Workshops\n• MLPDES26, Machine Learning and PDEs Workshop (2026)\n• FAU MoD Lecture: Hybrid Modeling and System Identification: Past and Future Directions by Prof. Dr. Helon Hultmann Ayala\n• FAU MoD Lecture: A long life: How desirable is it, evolutionarily speaking? by Prof. Dr. Hanna Kokko\n• FAU MoD Lecture: Bridging numerics and scientific machine learning for industrial applications by Dr. Christopher Straub\n• FAU MoD Workshop: FAU MoD Workshop (Dec. 2025) by Prof. Giovanni Fantuzzi | Prof. Denisa Martonova\n• FAU MoD Lecture: Quantum firmware: optimal control for quantum processors by Prof. Dr. Tommaso Calarco\n• FAU MoD Lecture: AI Components in PDE Solvers by Prof. Dr. Nils Thürey\n• FAU MoD Lecture: Disruption in science and engineering happens at scale by Prof. Dr. Johannes Brandstetter\n• FAU MoD Workshop: FAU MoD Workshop (Sep. 2025) by Prof. Lorenzo Liverani | Prof. Hagen Holthusen\n• FAU MoD Lecture: Exemplary applications of machine learning and optimization in quantum chemistry by Prof. Dr. Andreas Görling\n• FAU MoD Lecture & workshop: AI for maths and maths for AI by Dr. François Charton\n• FAU MoD Lecture: Optimization-based control for large-scale and complex systems: When and why does it work? by Prof. Dr. Lars Grüne\n• FAU MoD Lecture: Mathematics of neural stem cells: Linking data and processes by Prof. Dr. Ana Martin-Villalba\n• FAU MoD Lecture: FAU MoD Lecture S. Jin / N. Liu (double session) by Prof. Dr. Shi Jin and Prof. Dr. Nana Liu\n• FAU MoD Lecture: Do you think you understand sex and death? Why predictions about biological processes require more than just intuition by Prof. Dr. Hanna Kokko\n• FAU MoD Lecture: FAU MoD Lecture. Special December 2024 by Prof. Dr. Holger Rauhut and Prof. Dr. Christian Bär\n• FAU MoD Lecture: Measuring productivity and fixedness in lexico-syntactic constructions by Prof. Dr. Stephanie Evert\n• FAU MoD Lecture: New avenues for the interaction of computational mechanics and machine learning by Prof. Dr. Paolo Zunino\n• FAU MoD Lecture: Discovering and Communicating Excellence by Prof. Dr. Ute Klammer\n• FAU MoD Lecture: Thoughts on Machine Learning by Prof. Dr. Rupert Klein\n• FAU MoD Lecture: Using system knowledge for improved sample efficiency in data-driven modeling and control of complex technical systems by Prof. Dr. Sebastian Peitz\n• FAU MoD Lecture: Image Reconstruction – The Dialectic of Modelling and Learning by Prof. Dr. Martin Burger\n• FAU MoD Lecture: The role of Artificial Intelligence in the future of mathematics by Prof. Dr. Amaury Hayat\n• FAU MoD Lecture: FAU MoD Lecture. Special November 2023 by Prof. Dr. Michael Kohlhase and Prof. Dr. Edriss S. Titi\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 Karniadakis\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  \n_\nDon’t miss out our last news and connect with us!\nLinkedIn | Bluesky | Instagram | YouTube | X (Twitter)\n
URL:https://mod.fau.eu/events/fau-mod-lecture-data-driven-modeling-for-scientific-discovery-and-digital-twins/
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CATEGORIES:FAU MoD lecture,Seminar/Talk
LOCATION:FAU On-site / Online
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