ProBMoT is an implementation of the process-based modeling approach to modeling dynamical systems.

In the core of the process-based modeling approach is a formalism for representing models of dynamical systems as well as knowledge for modeling dynamical systems in a particular domain of interest. The process-based model formalism allows for representing models of dynamical systems at two levels of abstraction. At the higher level, the model is represented as sets of processes that govern the dynamics of the observed system and entities involved in the processes. At the lower level, each process includes a model of its dynamical influence on the variables of the observed system. The process-based modeling software can automatically combine the models of individual processes into a set of coupled differential equations used to simulate the behavior of the observed system. Thus, process-based models at the higher abstraction level reveal the structure of the observed systems in terms of entities and process interactions among them, providing explanation of the model behavior obtained by a lower-level declaration of the model equations.

To start establishing process-based models, we first have to formalize the modeling knowledge by establishing templates of generic entities that appear in the generic processes that govern the dynamics of systems in the particular domain. This modular knowledge representation allows for automated modeling of an observed system following a compositional approach. For a given modeling task, the generic templates are being instantiated into specific components (entities and processes) that can be used as building blocks for process-based models. Combinations of these building blocks represent candidate process-based models of the observed system. Automated modeling tool than searches for a process-based model with an optimal fit between the simulated and observed behavior of the system at hand.

Sašo Džeroski, Ljupčo Todorovski 2007. Equation discovery for systems biology: finding the structure and dynamics of biological networks from time course data. Current Opinion in Biotechnology, 19: 360-368.

Will Bridewell, Pat Langley, Ljupčo Todorovski, Sašo Džeroski 2008. Inductive process modeling. Machine Learning, 71: 1-32.

Darko Čerepnalkoski, Katerina Taškova, Ljupčo Todorovski, Nataša Atanasova, Sašo Džeroski, 2012. The influence of parameter fitting methods on model structure selection in automated modeling of aquatic ecosystems . Ecological Modelling, 45:136-165

Darko Čerepnalkoski, 2013. Process-Based Models of Dynamical Systems: Representation and Induction . Jožef Stefan International Postgraduate School, Ljubljana, Slovenia (PhD thesis)

Mateja Škerjanec, Nataša Atanasova, Darko Čerepnalkoski, Sašo Džeroski, Boris Kompare, 2014. Development of a knowledge library for automated watershed modeling . Environmental Modelling & Software, 54:60-72

Nikola Simidjievski, Ljupčo Todorovski, Sašo Džeroski, 2015. Learning ensembles of population dynamics models and their application to modelling aquatic ecosystems . Ecological Modelling, 306:305-317

Jovan Tanevski, Ljupčo Todorovski, Yannis Kalaidzidis, Sašo Džeroski, 2015. Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis . BMC Systems Biology, 9:31

Nikola Simidjievski, Ljupčo Todorovski, Sašo Džeroski, 2015. Predicting long-term population dynamics with bagging and boosting of process-based models. Expert Systems with Applications 42(22):8484-8496

Jovan Tanevski, Ljupčo Todorovski, Sašo Džeroski, 2016. Learning stochastic process-based models of dynamical systems from knowledge and data . BMC Systems Biology, 10:30

Nikola Simidjievski, Ljupčo Todorovski, Sašo Džeroski, 2016. Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models. PloS ONE 11(4):e0153507

Nikola Simidjievski, 2016. Ensembles of Process-Based Models of Dynamic Systems . Jožef Stefan International Postgraduate School, Ljubljana, Slovenia (PhD thesis)

Nikola Simidjievski, Ljupčo Todorovski, Sašo Džeroski, 2016. Learning Ensembles of Process-Based Models by Bagging of Random Library Samples . Discovery Science 2016. Lecture Notes in Computer Science 9956:245-260

Jovan Tanevski, Ljupčo Todorovski, Sašo Džeroski, 2016. Process-based design of dynamical biological systems . Scientific Reports, 6:34107

Jovan Tanevski, 2016. Deterministic and stochastic process-based modeling and design of dynamical systems in biology. Jožef Stefan International Postgraduate School, Ljubljana, Slovenia (PhD thesis)

Jovan Tanevski, Nikola Simidjievski, Ljupčo Todorovski, Sašo Džeroski, 2017. Process-Based Modeling and Design of Dynamical Systems . Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, 10536:378--382

Nikola Simidjievski*, Jovan Tanevski*, Bernard Ženko, Zoran Levnajić, Ljupčo Todorovski, Sašo Džeroski, 2018. Decoupling approximation robustly reconstructs directed dynamical networks . New Journal of Physics, 20:113003

Jovan Tanevski, Ljupčo Todorovski, Sašo Džeroski, 2020. Combinatorial search for selecting the structure of models of dynamical systems with equation discovery. Engineering Applications of Artificial Intelligence 8:103423

Nikola Simidjievski, Ljupčo Todorovski, Juš Kocijan, Sašo Džeroski, 2020. Equation Discovery for Nonlinear System Identification . IEEE Access, 8: 29930-29943

Matej Radinja, Mateja Škerjanec, Sašo Džeroski, Ljupčo Todorovski, and Nataša Atanasova, 2021. Design and Simulation of Stormwater Control Measures Using Automated Modeling. Water 13:2268.

Matej Radinja, Mateja Škerjanec, Mojca Šraj, Sašo Džeroski, Ljupčo Todorovski, and Nataša Atanasova, 2021. Automated modelling of urban runoff based on domain knowledge and equation discovery. Journal of Hydrology 603:127077.