The exploration of a system described by a non-linear, high-dimensional and stochastic computational model is a fundamental problem in all scientific disciplines relying on modeling and simulation. In this project we are interested in the scenario where a modeler has no or very limited prior knowledge about what type of qualitative interesting behavior the model can display over the large parameter space. The tools we develop should help the modeler discover those behaviors with a small computational budget, and as little manual work as possible. By utilizing human-in-the-loop machine learning we are developing a smart parameter sweep workflow. An example is shown in the image below, where a high-dimensional parameter sweep application is augmented with automated feature extraction and clustering, followed by training a model for classification based on user-defined labels (such as interesting or non-interesting realizations). With this model, the smart sweep application will learn to more efficiently explore areas of interestingness in the parameter space.
F. Wrede and A. Hellander (2018), Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning, BioRxiv (submitted).