  Use energy-based model for bridge-type innovation. The loss function is
explained by the game theory, the logic is clear and the formula is simple and
clear. Thus avoid the use of maximum likelihood estimation to explain the loss
function and eliminate the need for Monte Carlo methods to solve the normalized
denominator. Assuming that the bridge-type population follows a Boltzmann
distribution, a neural network is constructed to represent the energy function.
Use Langevin dynamics technology to generate a new sample with low energy
value, thus a generative model of bridge-type based on energy is established.
Train energy function on symmetric structured image dataset of three span beam
bridge, arch bridge, cable-stayed bridge, and suspension bridge to accurately
calculate the energy values of real and fake samples. Sampling from latent
space, using gradient descent algorithm, the energy function transforms the
sampling points into low energy score samples, thereby generating new bridge
types different from the dataset. Due to unstable and slow training in this
attempt, the possibility of generating new bridge types is rare and the image
definition of generated images is low.
