Physics-Informed Neural Networks for the Quantum Droplets in Binary Bose-Einstein Condensates

TL;DR

This paper demonstrates that Physics-Informed Neural Networks can effectively model and predict the structure and dynamics of quantum droplets in binary Bose-Einstein condensates, even under noisy data conditions.

Contribution

The study applies PINNs to quantum droplet dynamics, showing their accuracy, robustness, and ability to discover parameters in complex quantum systems.

Findings

PINNs accurately predict quantum droplet structures and dynamics.

PINNs demonstrate robustness with noisy data in parameter discovery.

Stable evolution of multipole quantum droplets is confirmed.

Abstract

Physics-Informed Neural Networks (PINNs), which integrate deep learning with physical prior knowledge, have proven to be a powerful tool for studying the dynamics of high-dimensional nonlinear systems. The present work utilizes PINNs to analyze the existence and evolution of quantum droplets (QDs) in a binary Bose-Einstein condensate (BEC), revealing the ability of this technique to accurately predict structural features of the QDs, their multipeak profiles, and dynamical behavior. The stable evolution of multipole QDs is thus demonstrated. Comparing different network architectures, including the training time, loss values, and $\mathbb{L_{2}}$ error, PINNs accurately predict specific dynamical characteristics of QDs. Furthermore, the PINN robustness is evaluated by the application of PINN to parameter-discovery tasks, considering both clean training data and data contaminated by $1\%$ random noise. The results highlight the efficiency of PINNs in modeling complex quantum systems and extracting reliable parameters under the noisy conditions.