Mer
30
Set
Seminari e Convegni
Connecting Materials Science and Continuum Theory for Smart Materials via Machine Learning
The seminar "Connecting Materials Science and Continuum Theory for Smart Materials via Machine Learning" will be held on 30 September 2026 at 2,30 pm, in Sala Ferrari, on the 2nd floor of DIMEAS, at Politecnico di Torino, and will be presented by Dr.-Ing. habil. Adrian Ehrenhofer, Institute of Solid Mechanics, Technische Universität Dresden.
Abstract
The specific pairing of materials and the layering setup determine which kind of functionality can be realized in a smart composite structure. Designing active-passive systems therefore connects the fields of materials science – with a focus on the Processing-Structure-Property-Performance relationship for predicting a material’s capabilities – to the field of continuum theory, which focuses on the geometry and functionality of a material in a specific setup.
The end-to-end engineering framework combines machine learning methods with continuum approaches, starting from abstract representations of synthesis and processing descriptors and ending with the actuation performance of a smart material in a specific composite functionality.
The methods combined range from (i) latent-space representations via embeddings, as used in Large Language Models, and (ii) classical feed-forward neural networks to (iii) continuum-based multi-field modelling with the Stimulus-Expansion Model and (iv) its integration into structural mechanics approaches.
The practical applicability of the approach will be illustrated through the case study of an active hydrogel-coated mesh that opens and closes in response to the level of hydration.
Biography
Dr.-Ing. habil. Adrian Ehrenhofer is a Research Group Leader at the Institute of Solid Mechanics at Technische Universität Dresden. His work connects multi-field modelling of smart structures, data-driven material discovery using classical machine learning and generative artificial intelligence, and multi-field biomechanics.
From September to October 2026, he will be visiting Prof. Erasmo Carrera’s research group at DIMEAS.
Abstract
The specific pairing of materials and the layering setup determine which kind of functionality can be realized in a smart composite structure. Designing active-passive systems therefore connects the fields of materials science – with a focus on the Processing-Structure-Property-Performance relationship for predicting a material’s capabilities – to the field of continuum theory, which focuses on the geometry and functionality of a material in a specific setup.
The end-to-end engineering framework combines machine learning methods with continuum approaches, starting from abstract representations of synthesis and processing descriptors and ending with the actuation performance of a smart material in a specific composite functionality.
The methods combined range from (i) latent-space representations via embeddings, as used in Large Language Models, and (ii) classical feed-forward neural networks to (iii) continuum-based multi-field modelling with the Stimulus-Expansion Model and (iv) its integration into structural mechanics approaches.
The practical applicability of the approach will be illustrated through the case study of an active hydrogel-coated mesh that opens and closes in response to the level of hydration.
Biography
Dr.-Ing. habil. Adrian Ehrenhofer is a Research Group Leader at the Institute of Solid Mechanics at Technische Universität Dresden. His work connects multi-field modelling of smart structures, data-driven material discovery using classical machine learning and generative artificial intelligence, and multi-field biomechanics.
From September to October 2026, he will be visiting Prof. Erasmo Carrera’s research group at DIMEAS.