BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv7.32.0//EN
X-ORIGINAL-URL:https://dcn.nat.fau.eu/
X-WR-CALNAME:
X-WR-CALDESC:FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship
X-WR-TIMEZONE:Europe/Berlin
BEGIN:VTIMEZONE
TZID:Europe/Berlin
X-LIC-LOCATION:Europe/Berlin
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T030000
RRULE:FREQ=YEARLY;BYMONTH=03;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=4SU
END:STANDARD
END:VTIMEZONE
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-507904e87e36bc5849ab6d3198183582@dcn.nat.fau.eu
DTSTART;TZID=Europe/Berlin:20250923T113000
DTEND;TZID=Europe/Berlin:20250923T123500
DTSTAMP:20250805T111932Z
CREATED:20250805
LAST-MODIFIED:20250821
PRIORITY:5
SEQUENCE:56
TRANSP:OPAQUE
SUMMARY:FAU MoD workshop L. Liverani / H. Holthusen
DESCRIPTION:Date: Tue. September 23, 2025\nEvent: FAU MoD Lecture\nOrganized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nSession 01: 11:30H\nHYCO: Hybrid-Cooperative Learning for PDEs\nSpeaker: Dr. Lorenzo Liverani\nAffiliation: FAU MoD, Research Center for Mathematics of Data | FAU DCN-AvH at Friedrich-Alexander-Universität Erlangen-Nürnberg\nAbstract. In this talk, I will present the Hybrid-Cooperative Learning strategy (HYCO), a modeling framework that iteratively integrates physics-based and data-driven models through a mutual regularization mechanism. \nUnlike traditional approaches that impose physical constraints directly on synthetic models, HYCO treats the physical and synthetic components as co-trained agents, nudging them towards mutual agreement. This cooperative learning scheme is naturally parallelizable and improves robustness to noise as well as to sparse or heterogeneous data. Through several numerical experiments on both static and time-dependent problems, I will demonstrate that HYCO is a promising architecture, outperforming classical physics-based and data-driven methods and recovering accurate solutions and model parameters even under ill-posed conditions.\nThis is ongoing work with E. Zuazua and M. Steynberg.\n  \nSession 02: 12:05H\nA Neural Network Companion to Inelasticity\nSpeaker: Dr.-Ing. Hagen Holthusen\nAffiliation: FAU MoD, Research Center for Mathematics of Data | Institute of Applied Mechanics at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nAbstract. Neural networks have become ubiquitous in science, helping us uncover patterns in complex data sets — from social interactions to medical image analysis.\nYet, their reliability often hinges on large amounts of training data, which is typically scarce in mechanics due to the high cost of experiments. Moreover, we are frequently interested in predicting the mechanical behaviour of solids beyond the range of observed data, a regime where conventional neural networks tend to perform poorly. To address these challenges, the community has proposed various strategies, with physics-informed neural networks being among the most widely discussed. \nIn this talk, however, we focus on physics-embedded neural networks for discovering the mechanics of solids that exhibit dissipative, irreversible behaviour — that is, where part of the stored energy cannot be recovered. Unlike physics-informed networks, which incorporate physical laws into the loss function, physics-embedded networks are architecturally constrained so that uncertainty-free physical principles are satisfied by design.  This architectural integration can improve generalisation, even when data are limited.\nImportantly, even within the same material class, inelastic behaviour can differ significantly — aluminium and titanium, for instance, show markedly different irreversible responses. This highlights the crucial trade-off between the interpretability and expressivity of neural networks when modelling complex, inelastic materials: a network must be flexible enough to capture material-specific features, yet structured enough to remain interpretable and physically meaningful.\nOUR SPEAKERS\nLorenzo Liverani has completed his PhD in 2023 under the supervision of prof. Vittorino Pata in Politecnico di Milano. He has a background in the long time behavior of partial differential equations and semigroup theory. After a one-year postdoc at Università Milano Bicocca, under the supervision of prof. Veronica Felli, he joined the DCN-AvH chair in February 2024 as a Humboldt Fellow. Now he works at the interface of machine learning and differential equations, focusing on Neural ODE architectures and hybrid techniques for PDE modeling.\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\nTue. September 23, 2025 at 11:00H (Berlin time)\nWHERE\nOn-site / Online\n[On-site]\nFriedrich-Alexander-Universität Erlangen-Nürnberg.\nRoom H12 Emmy-Noether-Hörsaal. Felix-Klein building. Mathematik/Informatik.\nGPS-Koord. Raum: 49.573704N, 11.030114E\n[Online]\nhttps://www.fau.tv/fau-mod-livestream-2025\n \nShortlink to share this event: https://go.fau.de/1c-gg \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://dcn.nat.fau.eu/events/fau-mod-workshop-sep-2025/
ORGANIZER;CN=FAU MoD:MAILTO:
CATEGORIES:FAU MoD Lecture,Seminar/Talk
LOCATION:Erlangen - Bavaria, Germany
ATTACH;FMTTYPE=image/png:https://dcn.nat.fau.eu/wp-content/uploads/FAUMoDworkshop_lLiverani_hHolthusen_23sep2025.png
END:VEVENT
END:VCALENDAR