# Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models

arXiv: https://arxiv.org/abs/2401.17118 · HTML: https://mlsanity.com/paper/2401.17118/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in MoE theory and gating functions, one of 12 directions in Mixture-of-experts.
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Placed in

- Mixture-of-experts / [MoE theory and gating functions](https://mlsanity.com/moe/moe-theory-and-gating-functions/)

## Builds on (most cited first, 4 of 4)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Fitting Jump Models](https://mlsanity.com/paper/1711.09220/) (2017)

## Built on by (most cited first, 1 of 1)

- [Explainable Condition Monitoring via Probabilistic Anomaly Detection Applied to Helicopter Transmissions](https://mlsanity.com/paper/2603.08130/) (2026)

## Abstract (arXiv)

Traditional models grounded in first principles often struggle with accuracy as the system's complexity increases. Conversely, machine learning approaches, while powerful, face challenges in interpretability and in handling physical constraints. Efforts to combine these models often often stumble upon difficulties in finding a balance between accuracy and complexity. To address these issues, we propose a comprehensive framework based on a "mixture of experts" rationale. This approach enables the data-based fusion of diverse local models, leveraging the full potential of first-principle-based priors. Our solution allows independent training of experts, drawing on techniques from both machine learning and system identification, and it supports both collaborative and competitive learning paradigms. To enhance interpretability, we penalize abrupt variations in the expert's combination. Experimental results validate the effectiveness of our approach in producing an interpretable combination of models closely resembling the target phenomena.