3 papers
physics.bio-ph2025
Error Bound Analysis of Physics-Informed Neural Networks-Driven T2 Quantification in Cardiac Magnetic Resonance Imaging
Mengxue Zhang, Qingrui Cai, Yinyin Chen +13
Physics-Informed Neural Networks (PINN) are emerging as a promising approach for quantitative parameter estimation of Magnetic Resonance Imaging (MRI). While existing deep learning…
physics.comp-ph2025
A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems
Xiangnan Yu, Hao Xu, Zhiping Mao +4
In complex physical systems, conventional differential equations often fall short in capturing non-local and memory effects, as they are limited to local dynamics and integer-order…
math.NA2025
Deep collocation method: A framework for solving PDEs using neural networks with error control
Mingxing Weng, Zhiping Mao, Jie Shen
Neural networks have shown significant potential in solving partial differential equations (PDEs). While deep networks are capable of approximating complex functions, direct one-sh…