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Training sophisticated machine learning (ML) models requires large datasetsthat are difficult or expensive to collect for many applications. If priorknowledge about system dynamics is available, mechanistic representations canbe used to supplement real-world data. We present SimbaML (Simulation-BasedML), an open-source tool that unifies realistic synthetic dataset generationfrom ordinary differential equation-based models and the direct analysis andinclusion in ML pipelines. SimbaML conveniently enables investigating transferlearning from synthetic to real-world data, data augmentation, identifyingneeds for data collection, and benchmarking physics-informed ML approaches.SimbaML is available from https://pypi.org/project/simba-ml/.

SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data