  Conservation of energy is at the core of many physical phenomena and
dynamical systems. There have been a significant number of works in the past
few years aimed at predicting the trajectory of motion of dynamical systems
using neural networks while adhering to the law of conservation of energy. Most
of these works are inspired by classical mechanics such as Hamiltonian and
Lagrangian mechanics as well as Neural Ordinary Differential Equations. While
these works have been shown to work well in specific domains respectively,
there is a lack of a unifying method that is more generally applicable without
requiring significant changes to the neural network architectures. In this
work, we aim to address this issue by providing a simple method that could be
applied to not just energy-conserving systems, but also dissipative systems, by
including a different inductive bias in different cases in the form of a
regularisation term in the loss function. The proposed method does not require
changing the neural network architecture and could form the basis to validate a
novel idea, therefore showing promises to accelerate research in this
direction.
