Computational Soft Material Mechanics Intelligence

• Applicant: Professor Dr.-Ing. Kevin Linka
• Subject Area: Mechanics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 533187597

Polymer materials are essential constituents not only of advanced engineered systems but also of everyday products like food, clothes, drugs and consumables, and also all biological organisms consist to a large extent of soft materials. Modeling the mechanical behavior of such materials has proven particularly challenging because they often exhibit complex types of nonlinearity, anisotropy and inelasticity. Descriptive modeling of soft materials therefore still often requires a large amount of expert knowledge. An even greater challenge are predictions how the behavior of soft materials can be tuned by subtle changes of their microstructure and processing. So far such predictions are possible only to a very limited extend and again with considerable expert knowledge. This project aims at leveraging most recent advances in physics-informed machine learning to automate both the descriptive and predictive modeling of soft materials to an unprecedented degree. Thereby, it will create a powerful basis for the future virtual design of soft materials. Moreover, by automating the pipeline from probing to describing to predicting the mechanical behavior of soft materials, the methods developed in in this project will enable robots to examine and handle also unknown and complex soft materials with an unprecedented degree of autonomy. This can be transformative for biomedical and biomechanical robotics, where, for example, surgical robots are facing in each patient or individual a different behavior of soft tissue and have to learn to probe and handle it. To showcase such benefits for medical robotics, this project will not only develop a toolbox of machine-learning based methods for describing and predicting the mechanical behavior of soft materials in a largely automated way but it will also illustrate their application in a simple robot demonstrator in the laboratory.

• DFG Programme: Independent Junior Research Groups
• International Connection: Austria, USA
• Cooperation Partners: Professor Dr.-Ing. Gerhard A. Holzapfel; Professor Jay Humphrey, Ph.D.; Professorin Dr.-Ing. Ellen Kuhl