Christopher Straub

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Fraunhofer IISB

AI-augmented Simulation

I am a Senior Scientist at the AI-augmented Simulation group at Fraunhofer IISB in Erlangen (Germany). I am leading the research activities on Scientific Machine Learning, focusing on

  • Physics-Informed Machine Learning and its industrial applications,
  • leveraging AI-based digital twins for industrial optimization, inverse design, and control,
  • methodological improvements of Physics-Informed Neural Networks, Neural Operators, and Foundation Models.

My work spans diverse application domains of Industrial AI, including semiconductor process simulations, lithography, battery modelling, and plastic deformation.

I have a background in mathematics, where I obtained my PhD in March 2024. My doctoral research was focused on the analysis of partial differential equations arising in galactic dynamics using mathematical and numerical methods as well as AI.

Recent news:

Dec 08, 2026 I am looking forward to giving the invited keynote talk at the Constrained Learning for Scientific Modelling session of the ELLIS UnConference 2026.
Nov 17, 2026 I am looking forward to taking part in the Speinshart Abbey workshop Machine Learning and Learning Systems: Modelling, PDEs, Numerics and Control.
Oct 18, 2026 Philipp Brendel will present our work on Electrode State-of-Health Estimation for Lithiom-Ion Batteries Using Physics-Informed Deep Operator Networks at this year’s IECON.
Sep 28, 2026 Ferdinand Schulz presented our work on Multi-Phase-Field Modeling of Thin-Film Silicidation at this year’s SISPAD.
Jul 23, 2026 Our paper Physics-informed operator learning for parameter estimation in lithium-ion-battery models enhanced by global experimental design and local identifiability analysis has been published at Energy and AI. Excellent collaboration with Andreas Rosskopf, Vincent Lorentz, and Felix Dietrich, led by Philipp Brendel. [Paper] [Blog post]