Jun 28, 2020 · In this paper, we show that the gradient descent method can identify a global minimizer of the least-squares optimization for solving second- ...
This paper shows that gradient descent can identify a global minimizer of the optimization problem with a well-controlled generalization error in the case ...
Aug 18, 2021 · In this paper, we show that the gradient descent method can identify a global minimizer of the least-squares optimization for solving second- ...
In this paper, we show that the gradient descent method can identify a global minimizer of the least-squares optimization for solving second-order linear PDEs ...
Dec 10, 2020 · In this paper, we show that the gradient descent method can identify a global minimizer of the least-squares optimization for solving second- ...
Dec 10, 2020 · PDF | The problem of solving partial differential equations (PDEs) can be formulated into a least-squares minimization problem, where neural ...
Duration: 1:00:27
Posted: Sep 22, 2021
Posted: Sep 22, 2021
People also ask
What is the best optimization algorithm for neural networks?
Can a two layer neural network represent the XOR function?
Which optimizer is best for LSTM?
What is gradient in machine learning?
... Partial Differential Equations via Deep Learning ... Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory.
Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory ... partial differential equations (PDEs) can be ...
The main aim of this paper is to conduct the con- vergence analysis of the gradient descent for two- layer physics-informed neural networks (PINNs).