# Difference between revisions of "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations"

From statwiki

(→Introduction) |
|||

Line 3: | Line 3: | ||

== Introduction == | == Introduction == | ||

− | + | ||

+ | In recent years, there has been an enormous growth in the amount of data and computing power available to researchers. Unfortunately, for many real-world scenarios, the cost of data acquisition is simply too high to collect an amount of data sufficient to guarantee robustness or convergence of training algorithms. In such situations, researchers are faced with the challenge of trying to generate results based on partial or incomplete datasets. The authors refer to these situations as \textit{small data regime} | ||

== Another Section == | == Another Section == | ||

Weeee | Weeee |

## Revision as of 13:52, 13 November 2020

## Presented by

Cameron Meaney

## Introduction

In recent years, there has been an enormous growth in the amount of data and computing power available to researchers. Unfortunately, for many real-world scenarios, the cost of data acquisition is simply too high to collect an amount of data sufficient to guarantee robustness or convergence of training algorithms. In such situations, researchers are faced with the challenge of trying to generate results based on partial or incomplete datasets. The authors refer to these situations as \textit{small data regime}

## Another Section

Weeee