Date: December 13, 2024, 12:00–13:30
Speaker: Dr.-Ing. Arnd Koeppe (Karlsruhe Institute of Technology)
Location:
• Innere Kanalstraße 15, 50823 Cologne
• Triforum (Google Maps, OpenStreetMap). Use north entrance next to Weinsbergstraße.
• Room 5.18 (left corridor coming from the staircase)
Title: Unifying Simulations, Research Data Management, and Artificial Intelligence
Abstract: Materials research progresses through iterative loops at multiple levels, from design, study, and optimization down to equilibrium iterations in physics-based simulations. Each level can be interpreted as a scientific workflow that enables efficient and guided investigations of research questions. These workflows exist in various forms, from traditional hand-written experimental protocols to software code and fully digitalized workflows supported by research data platforms like Kadi4Mat. Accelerating these loops is critical for advancing materials research, and Machine Learning (ML) and Artificial Intelligence (AI) offer powerful tools to enhance efficiency at all levels.
At the physics-based simulation level, iterative loops address, e.g., nonlinear material behavior and multi-scale phenomena. ML can considerably improve the performance of physics-based simulations by approximating complex nonlinear solutions, reducing computational costs, and enabling faster iteration. Neural networks, for instance, can be embedded into simulations as surrogate models, constitutive models, or hybrid approaches to achieve speed-ups while maintaining accuracy and flexibility. At the design, study, and optimization level, a tight integration of ML with Research Data Management (RDM), facilitates automated data preprocessing, pattern recognition, and hypothesis generation, enhancing the utility and interpretability of large datasets.
By integrating ML and AI into both simulations and RDM workflows, researchers can streamline their research processes and uncover insights more efficiently.
[1] A. Koeppe, F. Bamer, and B. Markert, "An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new Time-distributed Residual U-Net architecture," Computer Methods in Applied Mechanics and Engineering, vol. 366, p. 113088, Jul. 2020.
[2] D. Rajagopal et al., "Data-Driven Virtual Material Analysis and Synthesis for Solid Electrolyte Interphases," Advanced Energy Materials 2023, 13, p. 2301985, Sep. 2023.