25 citations · 26 across the 2 of their papers we have counts for
2 papers
physics.comp-ph2023★ 1 cited
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
Ruichen Li, Haotian Ye, Du Jiang +8
Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of exis…
cs.LG2022★ 25 cited
Is Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?
Chuwei Wang, Shanda Li, Di He +1
The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The Physics-Informed Loss is the d…