Solid Mechanics Lectures (2023)

Jan-2023

EUCLID: Learning constitutive models without stress data

Speaker: Sid Kumar (TU Delft, Netherlands)

Date: 31st Jan (Hybrid event at Engineering Building A: 1A.023 + Zoom)

Abstract of the lecture

Despite the recent advances in data-driven methods, constitutive modelling of materials remains embedded in a supervised setting where the stress-strain pairs are assumed to be available. However, in most common experimental setups, it is difficult to probe the entire stress-strain space, while getting such labelled data is expensive via multiscale simulations. The biggest challenge is – how does one even measure full stress tensors (forces are only boundary-averaged projections of stress tensors) for learning the stress-strain relations?

To bypass these challenges, we recently proposed a new data-driven framework called EUCLID which stands for – Efficient Unsupervised Constitutive Law Identification and Discovery (https://euclid-code.github.io/). The approach is unsupervised, i.e., it requires no stress data but only displacement and global force data, which are realistically available through mechanical testing and digital image correlation techniques. The problem of unsupervised discovery is solved by leveraging physical laws such as conservation of linear momentum in the bulk and at the loaded boundary of the domain. We discover physically interpretable models embodied by either – (i) parsimonious mathematical expressions discovered through sparse regression of a large catalogue of candidate functions, or (ii) ensemble of physics-consistent neural networks with higher generalization capability at the cost of analytical treatment. We demonstrate several benchmarks on the discovery of hyperelastic and elastoplastic constitutive models without using any stress data.

Feb-2023

Title TBD

Speaker: Ali Mehmanparast (University of Strathclyde)

Date: 28th Feb (Hybrid event at Engineering Building A: 1A.023 + Zoom)

Abstract of the lecture

Coming soon