3 papers
cond-mat.quant-gas2026
Physics-Informed Neural Networks for the Quantum Droplets in Binary Bose-Einstein Condensates
Dongshuai Liu, Boris A. Malomed, Wen Zhang
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-dimension…
quant-ph2024
Symmetry-guided gradient descent for quantum neural networks
Kaiming Bian, Shitao Zhang, Fei Meng +2
Many supervised learning tasks have intrinsic symmetries, such as translational and rotational symmetry in image classifications. These symmetries can be exploited to enhance perfo…
physics.optics2024
Physics-informed neural network for nonlinear dynamics of self-trapped necklace beams
Dongshuai Liu, Wen Zhang, Yanxia Gao +3
A physics-informed neural network (PINN) is used to produce a variety of self-trapped necklace solutions of the (2+1)-dimensional nonlinear Schrödinger/Gross-Pitaevskii equation.…