TN, ROM, ML, PINNs – Four approaches for real-time temperature estimation in electric motors in comparison
Henning Sauerland, Akiyasu Miyamoto, Anthony Ohazulike, Huihui Xu and Rik W. De Doncker

Adaptation of multi-fidelity optimization schemes to nonlinear structural dynamics applications
Arne Kaps, Tobias Lehrer, Koushyar Komeilizadeh and Fabian Duddeck

Reduced Order Models for Interdisciplinary Optimization of a Compressor Blade
Lisa Pretsch, Ilya Arsenyev and Fabian Duddeck

Physical Inspired Data-Driven Models using Evolutionary Approach
Somayeh Hosseinhashemi, Christoph Thon, Marvin Röhl and Carsten Schilde

Physics-aware convolutional neural networks for computational fluid dynamics simulations
Viktor Grimm, Alexander Heinlein and Axel Klawonn

Data-driven Machine Learning (ML) and Reduced Order Modeling (ROM) Approaches in Industrial Finite Element (FEA) Applications
Vasiliki Tsianika, Mariyappa Manohara and Kambiz Kayvantash

Resolving dispersion coefficients in reduced order chromatography models
Jayghosh S. Rao, Marek Behr and Eric von Lieres

Hybrid process modelling combining mechanistic equations with machine learning
Jazib Hassan, Jayghosh Rao and Eric von Lieres

Optimized graph-based methods for subsurface flow simulations
Stein Krogstad, Øystein Klemetsdal, August Johansson and Knut-Andreas Lie

A grey box approach for online process monitoring
Lars Bogaerts, Arnout Dejans, Patrick Van Rymenant, Matthias Faes and David Moens

A Data-Driven Reduced Order Modeling Approach Applied in Context of Numerical Analysis and Optimization of Plastic Profile Extrusion
Daniel Hilger and Norbert Hosters

In silico clinical trials: SILICOFCM – Coupling of physical based and data driven modeling
Nenad Filpovic