4 citations · 4 across the 8 of their papers we have counts for
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Domain-Decomposed Randomized Neural Networks for Partial Differential Equations in Unbounded Domains
Haixin Wang, Haoning Dang, Fei Wang +1
Partial differential equations on unbounded domains are challenging because the exterior region must be represented without excessive truncation error. Truncation-based methods oft…
Randomized Neural Networks for Integro-Differential Equations with Application to Neutron Transport
Haoning Dang, Fei Wang, Yifan Chen +3
Integro-differential equations arise in a wide range of applications, including transport, kinetic theory, radiative transfer, and multiphysics modeling, where nonlocal integral op…
Adaptive-Growth Randomized Neural Networks for Level-Set Computation of Multivalued Nonlinear First-Order PDEs with Hyperbolic Characteristics
Haoning Dang, Shi Jin, Fei Wang
This paper proposes an Adaptive-Growth Randomized Neural Network (AG-RaNN) method for computing multivalued solutions of nonlinear first-order PDEs with hyperbolic characteristics,…
Adaptive-Growth Randomized Neural Networks for PDEs: Algorithms and Numerical Analysis
Haoning Dang, Fei Wang, Song Jiang
Randomized neural network (RaNN) methods have been proposed for solving various partial differential equations (PDEs), demonstrating high accuracy and efficiency. However, initiali…
Local Randomized Neural Networks with Hybridized Discontinuous Petrov-Galerkin Methods for Stokes-Darcy Flows
Haoning Dang, Fei Wang
This paper introduces a new numerical approach that integrates local randomized neural networks (LRNNs) and the hybridized discontinuous Petrov-Galerkin (HDPG) method for solving c…
A conforming discontinuous Galerkin finite element method for Brinkman equations
Haoning Dang, Qilong Zhai, Zhongshu Zhao
In this paper, we present a conforming discontinuous Galerkin (CDG) finite element method for Brinkman equations. The velocity stabilizer is removed by employing the higher degree…