We present a method to increase the resolution of measurements of a physical system and subsequently predict its time evolution using thermodynamics-aware neural networks. Our method uses adversarial autoencoders, which reduce the dimensionality of the full order model to a set of latent variables that are enforced to match a prior, for example a normal distribution. Adversarial autoencoders are seen as generative models, and they can be trained to generate high-resolution samples from low-resolution inputs, meaning they can address the so-called super-resolution problem.

中文翻译：

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稀缺时间动力学数据的热力学超分辨率


我们提出了一种方法来提高物理系统测量的分辨率，并随后使用热力学感知神经网络来预测其时间演化。我们的方法使用对抗性自动编码器，它将全阶模型的维度减少为一组潜在变量，这些变量被强制匹配先验，例如正态分布。对抗性自动编码器被视为生成模型，它们可以被训练为从低分辨率输入生成高分辨率样本，这意味着它们可以解决所谓的超分辨率问题。