尝试从生成流的潜空间生成新型桥梁类型
An attempt to generate new bridge types from latent space of generative flow
AI总结：
本文利用 TensorFlow Probability 的 Glow API 训练归一化流，将四类桥梁图像的复杂分布映射到标准正态潜空间，并采样生成训练集中未出现的新型桥梁。
摘要：
本文通过不同分布之间坐标与概率变换的示例，以简单、简洁的方式介绍了归一化流（normalizing flow）的基本原理。文章从随机变量函数分布的角度解释了概率变换的本质，并引入了概率变换的缩放因子，即雅可比行列式。将数据集视为来自总体的样本，获得归一化流本质上是通过抽样调查来统计推断总体的数值特征，随后利用最大似然估计方法建立损失函数。本文介绍了归一化流如何巧妙地解决高维矩阵行列式计算和神经网络可逆变换这两大应用挑战。研究使用三跨梁桥、拱桥、斜拉桥和悬索桥的对称结构化图像数据集，基于 TensorFlow Probability 库中的 Glow API 构建并训练归一化流。该模型能够将桥梁数据集的复杂分布平滑地变换为标准正态分布，并从所得潜空间中采样，生成不同于训练数据集的新型桥梁类型。
英文摘要：
Through examples of coordinate and probability transformation between different distributions, the basic principle of normalizing flow is introduced in a simple and concise manner. From the perspective of the distribution of random variable function, the essence of probability transformation is explained, and the scaling factor Jacobian determinant of probability transformation is introduced. Treating the dataset as a sample from the population, obtaining normalizing flow is essentially through sampling surveys to statistically infer the numerical features of the population, and then the loss function is established by using the maximum likelihood estimation method. This article introduces how normalizing flow cleverly solves the two major application challenges of high-dimensional matrix determinant calculation and neural network reversible transformation. Using symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge, constructing and training normalizing flow based on the Glow API in the TensorFlow Probability library. The model can smoothly transform the complex distribution of the bridge dataset into a standard normal distribution, and from the obtained latent space sampling, it can generate new bridge types that are different from the training dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
