Digital twins require computationally-efficient reduced-order models (ROMs)
that can accurately describe complex dynamics of physical assets. However,
constructing ROMs from noisy high-dimensional data is challenging. In this
work, we propose a data-driven, non-intrusive method that utilizes stochastic
variational deep kernel learning (SVDKL) to discover low-dimensional latent
spaces from data and a recurrent version of SVDKL for representing and
predicting the evolution of latent dynamics. The proposed method is
demonstrated with two challenging examples -- a double pendulum and a
reaction-diffusion system. Results show that our framework is capable of (i)
denoising and reconstructing measurements, (ii) learning compact
representations of system states, (iii) predicting system evolution in
low-dimensional latent spaces, and (iv) quantifying modeling uncertainties.

使用基于数据驱动的随机变分深度核学习以及递归版本，提出了一种构建 ROMs 的方法，能够对物理资产的复杂动态进行精确描述，并具备去噪、重建、学习紧凑表征系统状态、预测以及量化建模不确定性的能力。

动力系统的循环深度核学习

Recurrent Deep Kernel Learning of Dynamical Systems

In this paper we provide framework to cope with two problems: (i) the
fragility of reinforcement learning due to modeling uncertainties because of
the mismatch between controlled laboratory/simulation and real-world conditions
and (ii) the prohibitive computational cost of stochastic optimal control. We
approach both problems by using reinforcement learning to solve the stochastic
dynamic programming equation. The resulting reinforcement learning controller
is safe with respect to several types of constraints constraints and it can
actively learn about the modeling uncertainties. Unlike exploration and
exploitation, probing and safety are employed automatically by the controller
itself, resulting real-time learning. A simulation example demonstrates the
efficacy of the proposed approach.

本文提供了一个应对强化学习的框架，解决了建模不确定性和计算成本高的问题，通过使用强化学习来解决随机动态规划方程，所得的强化学习控制器对多种类型的约束条件是安全的，并且可以主动学习建模不确定性，实现实时学习。通过模拟实例证明了提出方法的有效性。