668 citations · 4.9k across the 73 of their papers we have counts for
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An explainable operator approximation framework under the guideline of Green's function
Jianghang Gu, Ling Wen, Yuntian Chen +1
Traditional numerical methods, such as the finite element method and finite volume method, adress partial differential equations (PDEs) by discretizing them into algebraic equation…
Diffeomorphic Latent Neural Operators for Data-Efficient Learning of Solutions to Partial Differential Equations
Zan Ahmad, Shiyi Chen, Minglang Yin +4
A computed approximation of the solution operator to a system of partial differential equations (PDEs) is needed in various areas of science and engineering. Neural operators have…
Early Risk Assessment Model for ICA Timing Strategy in Unstable Angina Patients Using Multi-Modal Machine Learning
Candi Zheng, Kun Liu, Yang Wang +2
Background: Invasive coronary arteriography (ICA) is recognized as the gold standard for diagnosing cardiovascular diseases, including unstable angina (UA). The challenge lies in d…
Spectral Informed Neural Network: An Efficient and Low-Memory PINN
Tianchi Yu, Yiming Qi, Ivan Oseledets +1
With growing investigations into solving partial differential equations by physics-informed neural networks (PINNs), more accurate and efficient PINNs are required to meet the prac…
Discovery of Green's function based on symbolic regression with physical hard constraints
Jianghang Gu, Mengge Du, Yuntian Chen +1
The Green's function, serving as a kernel function that delineates the interaction relationships of physical quantities within a field, holds significant research implications acro…
Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression
Ding Wang, Yuntian Chen, Shiyi Chen
The rapid expansion of wind power worldwide underscores the critical significance of engineering-focused analytical wake models in both the design and operation of wind farms. Thes…