5 citations · 7 across the 3 of their papers we have counts for
3 papers
cond-mat.quant-gas2026
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
M. Doris, S. Guo, S. M. Koh +5
Here we describe the quantum gas analysis and inference (Q-GAIN) Python package, which enables rapid deployment of machine learning (ML) and physics-informed analysis techniques fo…
cond-mat.quant-gas2022★ 2 cited
Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research
Amilson R. Fritsch, Shangjie Guo, Sophia M. Koh +2
We establish a dataset of over experimental images of Bose--Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body phys…
cond-mat.quant-gas2021★ 5 cited
Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons
Shangjie Guo, Sophia M. Koh, Amilson R. Fritsch +2
In ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is parti…