论文标题
高通量科学发现的多模式数据的无监督物理知识分离
Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery
论文作者
论文摘要
我们介绍了物理知识的多模式自动编码器（PIMA） - 一个变异推理框架，用于在代表高通量测试的多模式科学数据集中发现共享信息。单个模式嵌入共享的潜在空间中，并通过专家配方的产品融合，从而在识别共享特征之前可以使高斯混合物融合。从集群中的采样可以允许跨模式生成建模，并结合了专家解码器施加了电感偏见的混合物，编码了先前的科学知识并赋予了潜在空间的结构化解散。这种方法可发现在高维异质数据集中可以检测到的指纹，从而避免了传统的与高保真测量和表征有关的传统瓶颈。由材料制造工艺加速的共设计和优化的动机，来自金属添加剂制造的晶格超材料的数据集证明了中尺度拓扑图像和机械应力 - 响应的图像之间的准确交叉模态推断。
We introduce physics-informed multimodal autoencoders (PIMA) - a variational inference framework for discovering shared information in multimodal scientific datasets representative of high-throughput testing. Individual modalities are embedded into a shared latent space and fused through a product of experts formulation, enabling a Gaussian mixture prior to identify shared features. Sampling from clusters allows cross-modal generative modeling, with a mixture of expert decoder imposing inductive biases encoding prior scientific knowledge and imparting structured disentanglement of the latent space. This approach enables discovery of fingerprints which may be detected in high-dimensional heterogeneous datasets, avoiding traditional bottlenecks related to high-fidelity measurement and characterization. Motivated by accelerated co-design and optimization of materials manufacturing processes, a dataset of lattice metamaterials from metal additive manufacturing demonstrates accurate cross modal inference between images of mesoscale topology and mechanical stress-strain response.
