Subject Area

Mathematics

Description

Major: Mathematics
Minor: Computer Science and Film

Faculty Mentor: Dr. Lynette Boos, Mathematics and Computer Science

We designed two neural networks that can learn how to classify three different types of partial differential equations (PDEs). Our data consists of numerical solutions to three categories of PDEs: Burger’s, Diffusion, and Transport equations. Using TensorFlow and the Keras library, we performed two tasks – the first a binary classification of Burger’s and Diffusion equation data, and the second a multi-label classification incorporating the Transport Equations as well. Our binary classification network requires vector labels to perform efficiently. Furthermore, our tertiary classification network continues to show that vector labeling provides the most accurate predictions. Our networks consistently make more accurate classifications and predictions than other classification tools, particularly Classification and Regression Trees (CART) and Support Vector Machine (SVM).

Publisher

Providence College

Date

4-22-2020

Start Date

4-22-2020 12:00 AM

Type

Poster

.pdf

Language

English

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.