5 citations · 5 across the 5 of their papers we have counts for
6 papers
Accelerated Learning of High Dimensional Functions with a Tensor-Featured Training Network
Karl Pierce, Yuehaw Khoo, Haizhao Yang
In this work we present a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN). This optimization procedure introduces conte…
Global Convergence and Error Propagation in Neural Gradient Flows: A Riemannian Optimization Framework
Shixin Zheng, Yiwei Wang, Haizhao Yang
We develop a geometric convergence theory for neural-network optimization within the minimizing movement scheme (MMS) framework. Reformulating each neural MMS step as a minimizatio…
Multi-Scale Finite Expression Method for PDEs with Oscillatory Solutions on Complex Domains
Gareth Hardwick, Haizhao Yang
Solving partial differential equations (PDEs) with highly oscillatory solutions on complex domains remains a challenging and important problem. High-frequency oscillations and intr…
Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method
Gareth Hardwick, Senwei Liang, Haizhao Yang
In this paper, we introduce a new finite expression method (FEX) to solve high-dimensional partial integro-differential equations (PIDEs). This approach builds upon the original FE…
On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver
Zhongzhan Huang, Senwei Liang, Hong Zhang +2
The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines. However, traditional numerical solvers are limited by the choice of…
Drop-Activation: Implicit Parameter Reduction and Harmonic Regularization
Senwei Liang, Yuehaw Khoo, Haizhao Yang
Overfitting frequently occurs in deep learning. In this paper, we propose a novel regularization method called Drop-Activation to reduce overfitting and improve generalization. The…