The field of optimal experimental design uses mathematical techniques to determine
experiments that are maximally informative from a given experimental setup. Here we apply …

Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities
govern various cellular processes. The limitations in genomics data and the complexity of …

Design is an inseparable part of most scientific and engineering tasks, including real and
simulation-based experimental design processes and parameter/hyperparameter …

Structural reliability analysis for small failure probabilities remains a challenging task,
despite the significant progress made by the active learning reliability methods (ALRMs) …

Aiming at the problem of ship-radiated noise feature extraction under the complex ocean
background, a novel nonlinear dynamic analysis method, named multi-scale reverse …

Time-variant reliability analysis can effectively estimate the safe state of structures under
dynamic uncertainties during their lifecycle. However, one of its key challenging issues is …

For 2-D causal systems, the variables depend both on time, and on spatial coordinates. This
article develops two identification algorithms for two-dimensional causal systems. First, a …

Stabilization of complex cyber-physical systems is extremely important in keeping the critical
infrastructure and the environment safe. This is, in particular, critical in coupled …

Deep learning based intelligent fault diagnosis methods have become a research hotspot in
the fields of fault diagnosis and the health management of rolling bearings in recent years …

In the pursuit of magnesium (Mg) alloys with targeted mechanical properties, a multi-
objective Bayesian optimisation workflow is presented to enable optimal Mg-alloy design. A …