# Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems

arXiv: https://arxiv.org/abs/2204.13376 · HTML: https://mlsanity.com/paper/2204.13376/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 67 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Discovering physical concepts with neural networks](https://mlsanity.com/paper/1807.10300/) (2018)
- [AI Poincar'e: Machine Learning Conservation Laws from Trajectories](https://mlsanity.com/paper/2011.04698/) (2020)
- [Deep neural networks for the evaluation and design of photonic devices](https://mlsanity.com/paper/2007.00084/) (2020)
- [Wave Physics as an Analog Recurrent Neural Network](https://mlsanity.com/paper/1904.12831/) (2019)
- [A Generative Model for Inverse Design of Metamaterials](https://mlsanity.com/paper/1805.10181/) (2018)
- [Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures](https://mlsanity.com/paper/1902.03865/) (2019)
- [Discovering conservation laws from trajectories via machine learning](https://mlsanity.com/paper/2102.04008/) (2021)
- [Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices](https://mlsanity.com/paper/1905.06889/) (2019)
- [Knowledge Discovery In Nanophotonics Using Geometric Deep Learning](https://mlsanity.com/paper/1909.07330/) (2019)

## Built on by (most cited first, 3 of 3)

- [Deep learning for the design of non-Hermitian topolectrical circuits](https://mlsanity.com/paper/2402.09978/) (2024)
- [GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers](https://mlsanity.com/paper/2402.10831/) (2024)
- [Quasiparticle Interference Kernel Extraction with Variational Autoencoders via Latent Alignment](https://mlsanity.com/paper/2506.05325/) (2025)

## Abstract (arXiv)

Non-Hermitian systems offer new platforms for unusual physical properties that can be flexibly manipulated by redistribution of the real and imaginary parts of refractive indices, whose presence breaks conventional wave propagation symmetries, leading to asymmetric reflection and symmetric transmission with respect to the wave propagation direction. Here, we use supervised and unsupervised learning techniques for knowledge acquisition in non-Hermitian systems which accelerate the inverse design process. In particular, we construct a deep learning model that relates the transmission and asymmetric reflection in non-conservative settings and proposes sub-manifold learning to recognize non-Hermitian features from transmission spectra. The developed deep learning framework determines the feasibility of a desired spectral response for a given structure and uncovers the role of effective gain-loss parameters to tailor the spectral response. These findings pave the way for intelligent inverse design and shape our understanding of the physical mechanism in general non-Hermitian systems.