Papers › SINDybrid: automatic generation of hybrid models for dynamic systems
SINDybrid: automatic generation of hybrid models for dynamic systems
Ulderico Di Caprio, M. Enis Leblebici
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Hybrid modelling enhances the accuracy and predictive capability of dynamic models by integrating first principles with data-driven methods, effectively mitigating epistemic uncertainties inherent in mechanistic approaches. However, hybrid model construction remains complex, typically requiring expert knowledge to identify model epistemic uncertainty and select suitable machine-learning components to capture it. This complexity limits broader adoption in research and industry. We introduce SINDybrid, an automated algorithm designed to streamline hybrid model development for dynamic systems. SINDybrid employs a mixed-integer linear programming (MILP) approach to systematically identify epistemic uncertainty sources and compensate them using optimally selected data-driven components. For broader accessibility and reproducibility, we provide SINDybrid as an open-source Python library. SINDybrid was validated through three case studies: a catalytic reaction system, a continuous fermentation process, and a Lotka-Volterra oscillator, each showcasing different epistemic uncertainties. The robustness of the algorithm is tested against varying experimental conditions, including measurement noise (up to 20%), limited training batches (minimum 2), and sparse temporal sampling (minimum 5 samples per experiment). Results demonstrate a consistent ability of the SINDybrid approach to produce accurate hybrid models, achieving R^2 scores above 0.85 on validation data. Additionally, the algorithm effectively identifies uncertainty locations within system dynamics under challenging scenarios. This work highlights SINDybrid potential as a versatile, automated solution for hybrid modelling, significantly reducing the barriers to adopting hybrid methods in complex dynamic systems across scientific research and industrial applications.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections