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Using Neural Networks To Classify Pdes, Julia Balukonis, Sabrina Fuller, Haley Rosso
Using Neural Networks To Classify Pdes, Julia Balukonis, Sabrina Fuller, Haley Rosso
Mathematics & Computer Science Student Scholarship
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 …