SOURCE-LINKED INTELLIGENCE

Polymer mechanics via Function spaces

CORDIS · observation · Publication date unknown

ulti-physics computation of polymers. However, complex mechanical behaviors of polymers present significant challenges, necessitating the development of unconventional continuum theories and advanced machine learning techniques. While the former facilitates the capture of both local and nonlocal behaviors of polymers, the latter ensures model reliability by utilizing rich information from large datasets. Unfortunately, current machine learning approaches, when coupled with immature continuum theories, suffer from limited interpretability and an ad hoc nature. This underscores the need for a theoretically grounded machine learning approach to drive genuine advance in human knowledge. PolyFun aims to enable machine learning not only to recognize pattern, but also to develop a deeper understanding of data through the principles of continuum mechanics and polymer physics. This will be achieved by representing polymers as reproducing kernel Hilbert spaces and defining their structural and topological properties via these physical insights. PolyFun consists of two stages. Stage 1 focuses o

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR