Abstract
AbstractPolaritonic lattices offer a unique testbed for studying nonlinear driven-dissipative physics. They show qualitative changes of their steady state as a function of system parameters, which resemble non-equilibrium phase transitions. Unlike their equilibrium counterparts, these transitions cannot be characterised by conventional statistical physics methods. Here, we study a lattice of square-arranged polariton condensates with nearest-neighbour coupling, and simulate the polarisation (pseudospin) dynamics of the polariton lattice, observing regions with distinct steady-state polarisation patterns. We classify these patterns using machine learning methods and determine the boundaries separating different regions. First, we use unsupervised data mining techniques to sketch the boundaries of phase transitions. We then apply learning by confusion, a neural network-based method for learning labels in a dataset, and extract the polaritonic phase diagram. Our work takes a step towards AI-enabled studies of polaritonic systems.
Funder
RCUK | Engineering and Physical Sciences Research Council
EC | Horizon 2020 Framework Programme
Icelandic Centre for Research
Russian Foundation for Basic Research
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy
Reference88 articles.
1. Carleo, G. et al. Machine learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019).
2. Mehta, P. et al. A high-bias, low-variance introduction to machine learning for physicists. Phys. Rep. 810, 1–124 (2019). A high-bias, low-variance introduction to machine learning for physicists.
3. Goodfellow, I. J. et al. Generative adversarial networks. Preprint at https://arxiv.org/abs/1406.2661 (2014).
4. Gomez-Uribe, C. A. & Hunt, N. The netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst. 6, 1–19 (2016).
5. Vaswani, A. et al. Attention is all you need. Preprint at https://arxiv.org/abs/1706.03762 (2017).
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