  Neural networks have been extensively applied to a variety of tasks,
achieving astounding results. Applying neural networks in the scientific field
is an important research direction that is gaining increasing attention. In
scientific applications, the scale of neural networks is generally
moderate-size, mainly to ensure the speed of inference during application.
Additionally, comparing neural networks to traditional algorithms in scientific
applications is inevitable. These applications often require rapid
computations, making the reduction of neural network sizes increasingly
important. Existing work has found that the powerful capabilities of neural
networks are primarily due to their non-linearity. Theoretical work has
discovered that under strong non-linearity, neurons in the same layer tend to
behave similarly, a phenomenon known as condensation. Condensation offers an
opportunity to reduce the scale of neural networks to a smaller subnetwork with
similar performance. In this article, we propose a condensation reduction
algorithm to verify the feasibility of this idea in practical problems. Our
reduction method can currently be applied to both fully connected networks and
convolutional networks, achieving positive results. In complex combustion
acceleration tasks, we reduced the size of the neural network to 41.7% of its
original scale while maintaining prediction accuracy. In the CIFAR10 image
classification task, we reduced the network size to 11.5% of the original
scale, still maintaining a satisfactory validation accuracy. Our method can be
applied to most trained neural networks, reducing computational pressure and
improving inference speed.
