Data-Driven Discovery of Governing Equations
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Pulchowk Campus
Abstract
Theoretical equations are the basis of scientific progress. Many scientific domains
still lack the appropriate theoretical model to reason about the phenomena. With the
rise of data, there is an increasing need for methodology in data-driven science and
engineering for understanding the physical phenomena. This thesis on Data-Driven
Discovery of Governing Equations aims to provide a method for model discovery and
find governing partial differential equations from data by training physics-informed
neural networks. Given data, our method generalizes a neural network to compute
a matrix of candidate terms for Partial Differential Equation(PDE). Minimizing the
residuals from the candidate matrix allows us to find the coefficients for the governing
equation. We present a framework to discover PDE not restricted to first-order time
derivative equations.
Description
Theoretical equations are the basis of scientific progress. Many scientific domains
still lack the appropriate theoretical model to reason about the phenomena.
Citation
MASTER IN MECHANICAL SYSTEMS DESIGN AND ENGINEERING