Mechanical metamaterials are meticulously designed structures with exceptional
mechanical properties determined by their microstructures and constituent materials …

Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …

The rise of machine learning has fueled the discovery of new materials and, especially,
metamaterials—truss lattices being their most prominent class. While their tailorable …

This paper introduces JAX-FEM, an open-source differentiable finite element method (FEM)
library. Constructed on top of Google JAX, a rising machine learning library focusing on high …

We applied physics-informed neural networks to solve the constitutive relations for
nonlinear, path-dependent material behavior. As a result, the trained network not only …

With the aid of recent efficient and prior knowledge-free machine learning (ML) algorithms,
extraordinary mechanical properties such as negative Poisson's ratio have extensively …

The inverse identification of heterogeneous composite properties from measured
displacement/strain fields is pivotal in engineering. Traditional methodologies and emerging …

Abstract Machine intelligence continues to rise in popularity as an aid to the design and
discovery of novel metamaterials. The properties of metamaterials are essentially …

Mechanical metamaterials are artificial structures that exhibit unusual mechanical properties
at the macroscopic level due to architected geometric design at the microscopic level. With …

Temperature effects are a major driver of strain and deformations in weather-exposed civil
infrastructure, such as bridges and buildings. For such structures, long-term temperature …