Building the Future: Data-Infused Constitutive Modeling of Soft Material
A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Polymer Physics and Theory".
Deadline for manuscript submissions: closed (10 March 2021) | Viewed by 5249
Special Issue Editor
Special Issue Information
Dear Colleagues,
Soft materials such as elastomers, sealants, hydrogels, shape-memory polymers, etc., exhibit strongly nonlinear behavior when subjected to external environmental or mechanical loads. Currently, with an abundance of data in each single experiment, classical modeling approaches have a hard time describing all the data, and in many applications, AI-based approaches have been used for some time due to the inherent complexity of deriving laws and equations to describe correlation between data. This Special Issue aims at exchanging and discussing the current theoretical, experimental, and computational advances in the predictive modeling of soft materials and machines using abundant data.
While recent advances in computational power and machine learning methods offer a novel insight into material modeling, reliable implementation of data-driven predictive models for soft materials remains a challenging task. With recent advances in characterization methods and in situ imaging techniques, extensive data are available on matrix behavior at multiple scales, which should be properly incorporated in the development of constitutive models. Interpretation of such a large database of noisy data has, so far, required a nontrivial solution, which motivates special data collection/handling techniques that are able to interpret/correlate billions of data points to achieve a clear picture of material behavior. Developing models and data processing techniques for incorporation/analysis of large datasets constitute major topics of this Special Issue. Similarly, machine learning, statistical learning, and data-driven approaches to replace or improve current continuum-based constitutive models are considered of equal importance.
Yours sincerely,
Dr. Roozbeh Dargazany
Guest Editor
Manuscript Submission Information
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Keywords
- Data-driven modeling
- Constitutive models
- Phenomenological models
- Microstructure data
- Data collection
- Continuum mechanics
- Microstructure data
