In conventional ODE modelling coefficients of an equation driving the system
state forward in time are estimated. However, for many complex systems it is
practically impossible to determine the equations or interactions governing the
underlying dynamics. In these settings, parametric ODE model cannot be
formulated. Here, we overcome this issue by introducing a novel paradigm of
nonparametric ODE modelling that can learn the underlying dynamics of arbitrary
continuous-time systems without prior knowledge. We propose to learn
non-linear, unknown differential functions from state observations using
Gaussian process vector fields within the exact ODE formalism. We demonstrate
the model's capabilities to infer dynamics from sparse data and to simulate the
system forward into future.

研究提出了一种基于高斯过程矢量场的非参数 ODE 建模方法，可以不需要先验知识学习任意连续时间系统的基本动力学，并利用稀疏数据推断系统的未来动态和进行模拟。

使用高斯过程学习未知的常微分方程模型

Learning unknown ODE models with Gaussian processes

Optimizing radio transmission power and user data rates in wireless systems
via power control requires an accurate and instantaneous knowledge of the
system model. While this problem has been extensively studied in the
literature, an efficient solution approaching optimality with the limited
information available in practical systems is still lacking. This paper
presents a reinforcement learning framework for power control and rate
adaptation in the downlink of a radio access network that closes this gap. We
present a comprehensive design of the learning framework that includes the
characterization of the system state, the design of a general reward function,
and the method to learn the control policy. System level simulations show that
our design can quickly learn a power control policy that brings significant
energy savings and fairness across users in the system.

本文介绍了一种利用强化学习优化功率控制和速率自适应的无线系统的新框架，包括系统状态特征的建模、奖励函数的设计和控制策略的学习方法，并通过系统级模拟验证了能够显著降低功耗并在系统用户之间实现公平的功率控制策略。