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:

Sep 28, 2026 We will present 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]
Jul 19, 2026 I was part of WCCM-ECCOMAS 2026 and presented our work on data-calibrated simulations of photoresist photoreactions via physics-informed neural operators.
Jun 15, 2026 I attended the PhysML Workshop 2026 to give a talk on our work on physics-informed fine-tuning of PDE foundation models.
May 12, 2026 We have published jNO, our open-source JAX library for Neural Operator and PDE Foundation Model training as well as FEM simulations. [GitHub repository] [Paper] [Blog post]