# Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks

arXiv: https://arxiv.org/abs/1809.00972 · HTML: https://mlsanity.com/paper/1809.00972/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science](https://mlsanity.com/paper/2110.08406/) (2021)
- [Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning](https://mlsanity.com/paper/2107.00088/) (2021)
- [OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures](https://mlsanity.com/paper/2305.11984/) (2023)
- [Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution Imaging](https://mlsanity.com/paper/2305.03177/) (2023)
- [Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization](https://mlsanity.com/paper/2506.20056/) (2025)
- [Interpretable inverse design of optical multilayer thin films based on extended neural adjoint and regression activation mapping](https://mlsanity.com/paper/2507.18644/) (2025)

## Abstract (arXiv)

Deep learning is known to be data-hungry, which hinders its application in many areas of science when datasets are small. Here, we propose to use transfer learning methods to migrate knowledge between different physical scenarios and significantly improve the prediction accuracy of artificial neural networks trained on a small dataset. This method can help reduce the demand for expensive data by making use of additional inexpensive data. First, we demonstrate that in predicting the transmission from multilayer photonic film, the relative error rate is reduced by 46.8% (26.5%) when the source data comes from 10-layer (8-layer) films and the target data comes from 8-layer (10-layer) films. Second, we show that the relative error rate is decreased by 22% when knowledge is transferred between two very different physical scenarios: transmission from multilayer films and scattering from multilayer nanoparticles. Finally, we propose a multi-task learning method to improve the performance of different physical scenarios simultaneously in which each task only has a small dataset.