# PhysiNet: A Combination of Physics-based Model and Neural Network Model for Digital Twins

arXiv: https://arxiv.org/abs/2106.14790 · HTML: https://mlsanity.com/paper/2106.14790/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning](https://mlsanity.com/paper/2004.11356/) (2020)
- [Machining Cycle Time Prediction: Data-driven Modelling of Machine Tool Feedrate Behavior with Neural Networks](https://mlsanity.com/paper/2106.09719/) (2021)

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

- [Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework](https://mlsanity.com/paper/2512.15767/) (2025)

## Abstract (arXiv)

As the real-time digital counterpart of a physical system or process, digital twins are utilized for system simulation and optimization. Neural networks are one way to build a digital twins model by using data especially when a physics-based model is not accurate or even not available. However, for a newly designed system, it takes time to accumulate enough data for neural network model and only an approximate physics-based model is available. To take advantage of both models, this paper proposed a model that combines the physics-based model and the neural network model to improve the prediction accuracy for the whole life cycle of a system. The proposed hybrid model (PhysiNet) was able to automatically combine the models and boost their prediction performance. Experiments showed that the PhysiNet outperformed both the physics-based model and the neural network model.