We introduce in this work the normalizing field flows (NFF) for learning random fields from scattered measurements. More precisely, we construct a bijective transformation (a normalizing flow characterizing by neural networks) between a Gaussian random field with the Karhunen-Lo\`eve (KL) expansion structure and the target stochastic field, where the KL expansion coefficients and the invertible networks are trained by maximizing the sum of the log-likelihood on scattered measurements. This NFF model can be used to solve data-driven forward, inverse, and mixed forward/inverse stochastic partial differential equations in a unified framework. We demonstrate the capability of the proposed NFF model for learning Non Gaussian processes and different types of stochastic partial differential equations.
翻译:我们在这项工作中引入了从分散测量中学习随机字段的正常化实地流动(NFF) 。 更确切地说, 我们用Karhunen- Lo ⁇ ⁇ éeve( KL) 扩展结构在高斯随机字段与目标随机字段之间构建了双向转换( 由神经网络进行正常化流动), 即 KL 扩展系数和可逆网络通过最大限度地利用分散测量的日志相似性之和来培训。 这个NFF 模型可用于在统一框架内解决数据驱动的前向、 反向和混合的前向/ 反对立部分差异方程式。 我们展示了拟议的NFF 模型学习非高斯进程和不同类型随机部分方程式的能力 。