Scientific Machine Learning Enabled Next Generation Design: application to bond lines in soft materi
Abstract
Approved for Public Release.We propose to employ machine learning within a revolutionary framework of model discovery that combines,clear box and black box modeling aspects in a way that uncovers new engineering theories from mechanistic insight into complex syste,ms. The new mathematical models, that the proposed method finds, will appeal to an engineers intuition and be amenable to the appli,cation of engineering judgment. Since these models will be expressed in the form of well understood mathematical constructions, they, can be employed as reduced order models, suitable for technological and design purposes. It is pointed out that one important goal,of the proposed work is to apply the later outlined methods to gain mechanistic insight and understanding regarding the complex phen,omenology observed within soft material (e.g. polymer) bond lines. We do this by learning partial differential equations (PDE), as w,ell as associated solution operators (e.g. Greens functions.) The structure of the PDE and Greens functions discovered with the pr,oposed method will offer deep and important clues regarding the mechanics of adhesive bond lines. Such mechanistic insight will be o,f great value and utility to domain experts when they design experiments and make performance predictions.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 10, 2021
- Source ID
- N000142212055
Entities
People
- Christopher Earls
Organizations
- Cornell University
- Office of Naval Research
- United States Navy