Introduction

基于DNN的替代模型的迁移学习效果尚未得到充分研究

Related works

首先介绍了PINN：控制方程以及初始和边界条件作为惩罚项嵌入损失函数中，以指导梯度下降方向；下面介绍了迁移学习

本文目标：在本文中，我们的目标不是优化网络设计，而是研究广泛使用的代理模型的传输能力。我们使用不同的模型对两个PDE基准进行了一系列实验。

Experimental results

下面第这一部分就是主要在说实验的东西了

3.1我们基于Helmholtz方程训练基于DNN的代理模型，并研究具有不同源项的情况下的迁移性能
3.2节中，我们对具有不同雷诺数的Navier-Stokes方程进行了迁移学习实验。
3.3节评估了两个不同方程之间的迁移能力。

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Transfer learning for deep neural network-based partial differential equations solving论文笔记

Introduction

Related works

Experimental results

Transfer learning for deep neural network-based partial differential equations solving论文笔记相关推荐

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