FAU MoD Lecture: Bridging numerics and scientific machine learning for industrial applications

Date: Mon. January 26, 2026
Event: FAU MoD Lecture
Organized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)

FAU MoD Lecture: FAU MoD Lecture: Bridging numerics and scientific machine learning for industrial applications
Speaker: Prof. Dr. Christopher Straub
Affiliation: Fraunhofer IIS (Germany)

Abstract. In this talk, we discuss digital solutions designed to address industry-relevant problems. We first review several solvers based on established numerical methods that provide reliable foundations for these digital solutions. We then turn to more recent approaches based on Scientific Machine Learning, in particular, Physics Informed Machine Learning, which integrates physics-based models with machine learning methods. We highlight in detail the potential and advantages of Physics Informed Machine Learning and demonstrate how these translate into improved solutions across different industrial use cases, including optical lithography, battery modelling, semiconductor process simulations, and plastic deformation.

OUR SPEAKER

Christopher Straub is a senior scientist at Fraunhofer IISB in Erlangen. He received his PhD in applied mathematics from the University of Bayreuth, where he studied PDEs arising in galactic dynamics using analytical and numerical methods. Since 2024, he has been part of the AI-augmented Simulation group within the department Modeling and AI at Fraunhofer IISB, where he now leads the research activities on Scientific Machine Learning. His research focuses on Physics‑Informed Machine Learning and its industrial applications.

AUDIENCE

This is a hybrid event (On-site/online) open to: Public, Students, Postdocs, Professors, Faculty, Alumni and the scientific community all around the world.

WHEN

Mon. January 26, 2026 at 11:30H (Berlin time)

WHERE

On-site / Online

 
Shortlink to share this event: https://go.fau.de/1cbhi

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