Unsupervised discovery of constitutive laws

We propose a new approach for data-driven automated discovery of constitutive laws. The approach is

Our method serves as a departure from supervised constitutive modeling using, e.g, neural networks and data-driven model-free approaches which show poor data-efficiency and generalization beyond training data.

Ref:  M. Flaschel*, S. Kumar*, L. De Lorenzis, Unsupervised discovery of interpretable hyperelastic constitutive laws, Computer Methods in Applied Mechanics and Engineering, 381 (2021), 113852

* equal contribution

TU Delft, Mekelweg 2, Delft 2628 CD, Netherlands