In active matter systems, individual constituents convert energy into non-conservative forces
or motion at the microscale, leading to morphological features and transport properties that …

Conspectus Machine learning has become a common and powerful tool in materials
research. As more data become available, with the use of high-performance computing and …

We present AI Poincaré, a machine learning algorithm for autodiscovering conserved
quantities using trajectory data from unknown dynamical systems. We test it on five …

Symbolic regression is a powerful technique to discover analytic equations that describe
data, which can lead to explainable models and the ability to predict unseen data. In …

The mechanical behavior of inelastic materials with microstructure is very complex and hard
to grasp with heuristic, empirical constitutive models. For this purpose, multiscale …

Identifying phase transitions is one of the key challenges in quantum many-body physics.
Recently, machine learning methods have been shown to be an alternative way of localising …

In the era of “big data”, artificial intelligence (AI) has emerged as an important approach in
the field of industrial intelligent diagnosis, owing to its powerful data mining and learning …

The development of a reliable subgrid-scale (SGS) model for large-eddy simulation (LES) is
of great importance for many scientific and engineering applications. Recently, deep …

We propose an interpretable deep learning (DL) model that extracts physical features from
turbulence data. Based on a conditional generative adversarial network combined with a …

Rich phenomena from complex systems have long intrigued researchers, and yet modeling
system micro-dynamics and inferring the forms of interaction remain challenging for …