论文标题
有条件gan对基于物理反向问题的后验推断的有效性和概括性
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems
论文作者
论文摘要
在这项工作中，我们训练有条件的Wasserstein生成对抗网络，从基于物理的贝叶斯推理问题的后部有效地采样。使用U-NET体系结构构建生成器，并使用条件实例标准化注入潜在信息。前者促进了多尺度逆图，而后者可以使潜在空间维度从测量的维度脱钩，并在U-NET的所有尺度上引入了随机性。我们解决了基于PDE的反问题，以证明我们的方法在量化推断领域的不确定性时的性能。此外，我们表明发电机可以学习本质上本地的逆图，这反过来促进了使用分布式样本测试时的普遍性。
In this work, we train conditional Wasserstein generative adversarial networks to effectively sample from the posterior of physics-based Bayesian inference problems. The generator is constructed using a U-Net architecture, with the latent information injected using conditional instance normalization. The former facilitates a multiscale inverse map, while the latter enables the decoupling of the latent space dimension from the dimension of the measurement, and introduces stochasticity at all scales of the U-Net. We solve PDE-based inverse problems to demonstrate the performance of our approach in quantifying the uncertainty in the inferred field. Further, we show the generator can learn inverse maps which are local in nature, which in turn promotes generalizability when testing with out-of-distribution samples.
