6 citations · 12 across the 6 of their papers we have counts for
6 papers
Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification
Zirui Deng, Jingbo Sun, Deyu Meng +1
Repeatedly solving parametric PDEs is essential for uncertainty quantification, design optimization and inverse problems, but conventional neural operators require expensive non-co…
Randomized Neural Networks for Partial Differential Equation on Static and Evolving Surfaces
Jingbo Sun, Fei Wang
Surface partial differential equations arise in numerous scientific and engineering applications. Their numerical solution on static and evolving surfaces remains challenging due t…
Local Randomized Neural Networks with Discontinuous Galerkin Methods for KdV-type and Burgers Equations
Jingbo Sun, Fei Wang
The Local Randomized Neural Networks with Discontinuous Galerkin (LRNN-DG) methods, introduced in [42], were originally designed for solving linear partial differential equations.…
Local Randomized Neural Networks with Discontinuous Galerkin Methods for Diffusive-Viscous Wave Equation
Jingbo Sun, Fei Wang
The diffusive-viscous wave equation is an advancement in wave equation theory, as it accounts for both diffusion and viscosity effects. This has a wide range of applications in geo…
Local Randomized Neural Networks with Discontinuous Galerkin Methods for Partial Differential Equations
Jingbo Sun, Suchuan Dong, Fei Wang
Randomized neural networks (RNN) are a variation of neural networks in which the hidden-layer parameters are fixed to randomly assigned values and the output-layer parameters are o…
Deep Petrov-Galerkin Method for Solving Partial Differential Equations
Yong Shang, Fei Wang, Jingbo Sun
Deep neural networks are powerful tools for approximating functions, and they are applied to successfully solve various problems in many fields. In this paper, we propose a neural…