  Differential equations are used to model problems that originate in
disciplines such as physics, biology, chemistry, and engineering. In recent
times, due to the abundance of data, there is an active search for data-driven
methods to learn Differential equation models from data. However, many
numerical methods often fall short. Advancements in neural networks and deep
learning, have motivated a shift towards data-driven deep learning methods of
learning differential equations from data. In this work, we propose a
forward-Euler based neural network model and test its performance by learning
ODEs such as the FitzHugh-Nagumo equations from data using different number of
hidden layers and different neural network width.
