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