论文标题
物理知识的变异自动编码器，可改善对环境因素的鲁棒性
Physics-informed Variational Autoencoders for Improved Robustness to Environmental Factors of Variation
论文作者
论文摘要
机器学习模型与物理模型的结合是学习强大数据表示的最新研究路径。在本文中，我们介绍了P $^3 $ vae，这是一种多种自动编码器，它整合了与数据采集条件相关的对变异的潜在因素的先前物理知识。 p $^3 $ vae将标准神经网络层与不可验证的物理层相结合，以部分地将潜在空间扎根于物理变量。我们介绍了一种半监督的学习算法，该算法在机器学习部分和物理部分之间取得了平衡。关于模拟和真实数据集的实验证明了我们框架在外推能力和可解释性方面与竞争性物理知识和常规机器学习模型的好处。特别是，我们表明p $^3 $ vae自然具有有趣的分解功能。我们的代码和数据已在https://github.com/romain3ch216/p3vae上公开提供。
The combination of machine learning models with physical models is a recent research path to learn robust data representations. In this paper, we introduce p$^3$VAE, a variational autoencoder that integrates prior physical knowledge about the latent factors of variation that are related to the data acquisition conditions. p$^3$VAE combines standard neural network layers with non-trainable physics layers in order to partially ground the latent space to physical variables. We introduce a semi-supervised learning algorithm that strikes a balance between the machine learning part and the physics part. Experiments on simulated and real data sets demonstrate the benefits of our framework against competing physics-informed and conventional machine learning models, in terms of extrapolation capabilities and interpretability. In particular, we show that p$^3$VAE naturally has interesting disentanglement capabilities. Our code and data have been made publicly available at https://github.com/Romain3Ch216/p3VAE.
