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X-WR-CALDESC:DeustoCCM - Chair of Computational Mathematics at University of Deusto
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UID:MEC-ef8ff9dcba0ea0f66f02b9d355fb860c@cmc.deusto.eus
DTSTART:20251027T150000Z
DTEND:20251027T160000Z
DTSTAMP:20251031T222500Z
CREATED:20251031
LAST-MODIFIED:20251031
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:FAU MoD Lecture: Finding the optimal model complexity of whole-brain models and digital twins
DESCRIPTION:Date: Mon. October 27, 2025\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: Finding the optimal model complexity of whole-brain models and digital twins\nSpeaker: Prof. Dr. Xenia Kobeleva\nAffiliation: Department of Neurostimulation, Ruhr-Universität Bochum (Germany)\nAbstract.  Whole-brain neural mass models can effectively simulate neural activity, however the elevated model complexity of some implementations might hinder their translation to clinical practice, e.g. for digital twin applications. In this talk we will talk about different strategies to choose the optimal level of complexity and critically evaluate potential added benefit of more sophisticated heterogeneous models. These results might facilitate the translation of simpler and less computationally complex models to clinical applications, while maintaining the same accuracy for predictions.\nOUR SPEAKER\nXenia Kobeleva is an assistant professor (tenure track) of Neurostimulation at Ruhr University Bochum’s Faculty of Medicine. She is an expert in neurodegenerative diseases and brain modelling and affiliated with several leading brain research institutions, such as UPF’s computational neuroscience lab and the German Center for Neurodegenerative Diseases (DZNE).\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. October 27, 2025 at 16:00H (Berlin time)\nWHERE\nOn-site / Online\n[On-site]\nFriedrich-Alexander-Universität Erlangen-Nürnberg.\nRoom H20. 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 \nLink to share this event: https://go.fau.de/1c-bg \nThis event @LinkedIn\n \nYou might like:\n• FAU MoD Lectures\n• Upcoming events\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://cmc.deusto.eus/events-calendar/fau-mod-lecture-finding-the-optimal-model-complexity-of-whole-brain-models-and-digital-twins/
ORGANIZER;CN=FAU MoD:MAILTO:
CATEGORIES:FAU MoD Lecture,Seminar/Talk
LOCATION:Erlangen - Bavaria, Germany
ATTACH;FMTTYPE=image/png:https://cmc.deusto.eus/wp-content/uploads/FAUMoDLecture_xeniaKobeleva_27oct2025_img.png
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