2 citations · 5 across the 13 of their papers we have counts for
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Runge-Kutta Random Feature Method for Solving Multiphase Flow Problems of Cells
Yangtao Deng, Qiaolin He
Cell collective migration plays a crucial role in a variety of physiological processes. In this work, we propose the Runge-Kutta random feature method to solve the nonlinear and st…
Solving Multi-Group Neutron Diffusion Eigenvalue Problem with Decoupling Residual Loss Function
Shupei Yu, Qiaolin He, Shiquan Zhang +3
In the midst of the neural network's success in solving partial differential equations, tackling eigenvalue problems using neural networks remains a challenging task. However, the…
A novel number-theoretic sampling method for neural network solutions of partial differential equations
Yu Yang, Pingan He, Xiaoling Peng +1
Traditional Monte Carlo integration using uniform random sampling exhibits degraded efficiency in low-regularity or high-dimensional problems. We propose a novel deep learning fram…
Deep FBSDE Neural Networks for Solving Incompressible Navier-Stokes Equation and Cahn-Hilliard Equation
Yangtao Deng, Qiaolin He
Efficient algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the curse of dimensionality.…