2 citations · 2 across the 2 of their papers we have counts for
3 papers
physics.comp-ph2026★ 2 cited
Discontinuity-aware KAN-based physics-informed neural networks
Guoqiang Lei, D. Exposito, Xuerui Mao
Physics-informed neural networks (PINNs) have proven to be a promising method for the rapid solving of partial differential equations (PDEs) in both forward and inverse problems. H…
physics.comp-ph2026
Discontinuity-aware physics-informed neural network for phase-field method in three-phase flow with phase change
Guoqiang Lei, Zhihua Wang, Lijing Zhou +2
Physics-informed neural networks (PINNs) have been applied to simulate multiphase flows, yet they are limited in modeling phase changes and sharp interfaces due to optimization con…
cs.CV2024
Popeye: A Unified Visual-Language Model for Multi-Source Ship Detection from Remote Sensing Imagery
Wei Zhang, Miaoxin Cai, Tong Zhang +3
Ship detection needs to identify ship locations from remote sensing (RS) scenes. Due to different imaging payloads, various appearances of ships, and complicated background interfe…