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DTSTART;TZID=Europe/Berlin:20251210T113000
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SUMMARY:FAU MoD Workshop G. Fantuzzi / D. Martonova
DESCRIPTION:Date: Wed. December 10, 2025\nEvent: FAU MoD Workshop\nOrganized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nSession 01: 11:30H\nLyapunov meets Koopman: a new approach to data-driven analysis of nonlinear dynamics\nSpeaker: Prof. Dr. Giovanni Fantuzzi\nAffiliation: FAU MoD, Research Center for Mathematics of Data | FAU DCN-AvH at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nAbstract. Lyapunov frameworks have been used for decades to analyze the performance of nonlinear dynamical systems with known mathematical models. The Koopman operator framework has instead recently gained popularity for studying nonlinear dynamics from data. This talk will explain how these two frameworks can be unified into a groundbreaking data-driven Koopman-Lyapunov approach for analyzing nonlinear dynamics, which can answer a much wider range of dynamical systems questions compared to classical Koopman methods alone. I will also discuss perspectives and open challenges related to the implementation and mathematical analysis of this Koopman-Lyapunov approach.\n  \nSession 02: 12:05H\nData-driven constitutive modeling for soft biological tissues\nSpeaker: Dr. Denisa Martonová\nAffiliation: FAU MoD, Research Center for Mathematics of Data | Institute of Applied Mechanics at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nAbstract. An accurate description of mechanical behavior in biological tissues relies on the formulation of suitable constitutive models. This talk will explain how data-driven approaches can automate the discovery of such models directly from experimental measurements. We introduce invariant-based and principal-stretch-based constitutive neural networks that embed physical constraints and recover interpretable formulations for hyperelastic materials. We then extend this concept to generalized-invariant-based constitutive neural networks, which simultaneously learn both the invariant representation and the underlying model. Finally, we outline a complementary database-driven method that bypasses numerical optimization and rapidly identifies constitutive behavior through pattern recognition. Together, these approaches illustrate how automated, physics-embedded model discovery can enhance modeling in biomechanics and beyond.\nOUR SPEAKERS\nGiovanni Fantuzzi is a W1 Professor in the Department of Mathematics at Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg. Before joining FAU, Prof. Fantuzzi held an Imperial College Research Fellowship in the Department of Aeronautics, where he also received a PhD and a Master Eng. in Aeronautics degrees. Alongside his PhD, he held a research position in Engineering Science at the University of Oxford. He was awarded a Geophysical Fluid Dynamics Fellowship at WHOI (2015) and an EPSRC Doctoral Prize Fellowship (2018).\nHis work spans optimization, dynamical systems, fluid mechanics, and partial differential equations (PDEs), in particular, nonlinear differential equations using a mix of mathematical analysis and numerical tools for convex optimization. \nDenisa Martonová is a researcher at the Institute of Applied Mechanics (LTM) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). She also completed her doctoral studies at FAU, where her research focused on computational modeling and simulation of myocardial tissue. Her current work focuses on data-driven constitutive modeling, with a particular emphasis on soft biological materials and hyperelastic tissue behavior.\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\nWed. December 10, 2025 at 11:30H (Berlin time)\nWHERE\nOn-site / Online\n[On-site]\nFriedrich-Alexander-Universität Erlangen-Nürnberg.\nRoom H21. ER – Südgelände. Technische Fakultät.\nCauerstraße 5b, 91058, Erlangen. Bavaria (Germany)\nGPS-Koord. Raum (gMaps): 49.57375712076829, 11.028432695446526\n[Online]\nhttps://www.fau.tv/clip/id/59621\n \nShortlink to share this event: https://go.fau.de/1cca- \nThis event @LinkedIn\n \nYou might like:\n• FAU MoD Lectures\n• Upcoming events\n• FAU MoD Courses & Workshops\n• FAU MoD Lecture: Bridging numerics and scientific machine learning for industrial applications by Prof. Dr. Christopher Straub\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 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://dcn.nat.fau.eu/events/faumod-workshop-dec-2025/
ORGANIZER;CN=FAU MoD:MAILTO:
CATEGORIES:FAU MoD Lecture,Seminar/Talk
LOCATION:Erlangen - Bavaria, Germany
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