7 citations · 12 across the 9 of their papers we have counts for
9 papers
Normalized Fourier-induced PINN method for solving the wave propagation equation in a non-unitized domain over an extended time range
Jichao Ma, Dandan Liu, Jinran Wu +1
Physics-Informed Neural Networks (PINNs) have gained significant attention for their simplicity and flexibility in engineering and scientific computing. In this study, we introduce…
A Differential Index Measuring Rater's Capability in Educational Assessment
Y. -G. Wang, J. Wu, X. Qiu
A rater's ability to assign accurate scores can significantly impact the outcomes of educational assessments. However, common indices for evaluating rater characteristics typically…
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
Yilong Hou, Xi'an Li, Jinran Wu +1
Bayesian Physics Informed Neural Networks (BPINN) have attracted considerable attention for inferring the system states and physical parameters of differential equations according…
Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study
Jin Yuan, Xuelan Qiu, Jinran Wu +3
The interest in predicting online learning performance using ML algorithms has been steadily increasing. We first conducted a scientometric analysis to provide a systematic review…
Augmented physics informed extreme learning machine to solve the biharmonic equations via Fourier expansions
Xi'an Li, Jinran Wu, Yujia Huang +4
To address the sensitivity of parameters and limited precision for physics-informed extreme learning machines (PIELM) with common activation functions, such as sigmoid, tangent, an…
Solving a class of multi-scale elliptic PDEs by means of Fourier-based mixed physics informed neural networks
Xi'an Li, Jinran Wu, You-Gan Wang +2
Deep neural networks have garnered widespread attention due to their simplicity and flexibility in the fields of engineering and scientific calculation. In this study, we probe int…
Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions
Xi'an Li, Jiaxin Deng, Jinran Wu +3
Deep learning methods have gained considerable interest in the numerical solution of various partial differential equations (PDEs). One particular focus is physics-informed neural…
Predication of Inflection Point and Outbreak Size of COVID-19 in New Epicentres
Qibin Duan, Jinran Wu, Gaojun Wu +1
The coronavirus disease 2019 (COVID-19) had caused more that 8 million infections as of middle June 2020. Recently, Brazil has become a new epicentre of COVID-19, while India and A…
A working likelihood approach to support vector regression with a data-driven insensitivity parameter
Jinran Wu, You-Gan Wang
The insensitive parameter in support vector regression determines the set of support vectors that greatly impacts the prediction. A data-driven approach is proposed to determine an…