4 papers
A Boson-informed neural network for Ground States of Many-Body Schrödinger equations
Zhizhong Kong, Jerry Zhijian Yang, Cheng Yuan +1
We propose a symmetry-preserving Boson-informed neural network (BINN) for the variational Monte Carlo computation of interacting-boson ground states in continuous space. The framew…
Multi-stage neural operator learning with application for convolutions
Zhiping Mao, Zhenye Wen, Yong Zhang +1
Convolution integrals widely exist in applications, and to enable fast and accurate computations, this paper introduces two general multi-stage neural operator learning frameworks.…
Unsupervised operator learning approach for dissipative equations via Onsager principle
Zhipeng Chang, Zhenye Wen, Xiaofei Zhao
Existing operator learning methods rely on supervised training with high-fidelity simulation data, introducing significant computational cost. In this work, we propose the deep Ons…
Computing ground states of Bose-Einstein condensation by normalized deep neural network
Weizhu Bao, Zhipeng Chang, Xiaofei Zhao
We propose a normalized deep neural network (norm-DNN) for computing ground states of Bose-Einstein condensation (BEC) via the minimization of the Gross-Pitaevskii energy functiona…