SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data

2023/04/08 by Kleissl, Maximilian, Lukas Drews, Drews, Lukas +12 · 1 citation
Computer Science · Materials Science · Physics and Astronomy · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning in Materials Science #Model Reduction and Neural Networks

paper · pdf · doi:10.48550/arxiv.2304.04000

openalex publication_date 2023/04/08 · openalex created_date 2023/04/12 · openalex updated_date 2026/07/28

Abstract

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/.

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