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.

翻译：采用基于能量的模型进行桥梁类型创新。通过博弈论解释损失函数，逻辑清晰且公式简洁明了。因此避免了使用最大似然估计来解释损失函数，也无需蒙特卡洛方法求解归一化分母。假设桥梁类型总体服从玻尔兹曼分布，构建神经网络表示能量函数。利用朗之万动力学技术生成具有低能量值的新样本，从而建立基于能量的桥梁类型生成模型。在三跨梁桥、拱桥、斜拉桥和悬索桥的对称结构图像数据集上训练能量函数，以精确计算真实样本和虚假样本的能量值。从潜在空间中采样，通过梯度下降算法，能量函数将采样点转化为低能量分数样本，进而生成与数据集不同的新型桥梁结构。由于本次尝试中训练不稳定且速度缓慢，生成新型桥梁结构的可能性较低，且生成图像的清晰度不足。