The paper surveys automated scientific discovery, from equation discovery and symbolic
regression to autonomous discovery systems and agents. It discusses the individual …

Process-based modeling is an approach to learning understandable, explanatory models of
dynamic systems from domain knowledge and data. Although their utility has been proven …

Modelling tools are widely used to analyse the urban drainage systems and to simulate the
effects of future urban development and stormwater control measures. Usually, these tools …

A foremost challenge in modern network science is the inverse problem of reconstruction
(inference) of coupling equations and network topology from the measurements of the …

Ensembles are a well established machine learning paradigm, leading to accurate and
robust models, predominantly applied to predictive modeling tasks. Ensemble models …

Parameter estimation in dynamic models of ecosystems is essentially an optimization task.
Due to the characteristics of ecosystems and typical models thereof, such as non-linearity …

Background Identifying a proper model structure, using methods that address both structural
and parameter uncertainty, is a crucial problem within the systems approach to biology. And …

Equation discovery methods enable modelers to combine domain-specific knowledge and
system identification to construct models most suitable for a selected modeling task. The …

Automated modeling aims at the induction of mathematical models, both their structure and
parameter values, from time-series measurements of observed system variables. In this …

Ecological modelling is increasing in importance to facilitate the development of sustainable
management planning of terrestrial ecosystems and integrate social and economic …