All physical laws are described as mathematical relationships between state variables.
These variables give a complete and non-redundant description of the relevant system …

Dynamic Bayesian networks provide a compact and natural representation for complex
dynamic systems. However, in many cases, there is no expert available from whom a model …

A major challenge in the study of dynamical systems is that of model discovery: turning data
into models that are not just predictive, but provide insight into the nature of the underlying …

Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

Extracting governing equations from data is a central challenge in many diverse areas of
science and engineering. Data are abundant whereas models often remain elusive, as in …

For centuries, scientists have attempted to identify and document analytical laws that
underlie physical phenomena in nature. Despite the prevalence of computing power, the …

Dynamical models underpin our ability to understand and predict the behaviour of natural
systems. Whether dynamical models are developed from first-principles derivations or from …

The automated discovery of physical laws from discrete noisy data is significant for
evaluating the response, stability, and reliability of dynamic systems. In contract to the …

In the absence of governing equations, dimensional analysis is a robust technique for
extracting insights and finding symmetries in physical systems. Given measurement …

Experimental data are often affected by uncontrolled variables that make analysis and
interpretation difficult. For spatiotemporal systems, this problem is further exacerbated by …