We show that a reservoir computer is an effective tool for model-free
prediction of extreme events in deterministic chaotic systems. This prediction
allows us to suppress unwanted extreme events, by applying weak control
perturbations to the system at times preceding expected extreme events. The
effectiveness of such a prediction and prevention strategy is demonstrated for
a system of globally coupled FitzHugh-Nagumo neurons and for a system of two
almost identical unidirectionally coupled chaotic oscillators.

本研究证明，水库计算机是一种有效的无模型预测确定性混沌系统中极端事件的工具。通过在预期极端事件之前的某些时刻对系统施加弱控制扰动，我们可以抑制不良的极端事件。本文在全球耦合的 FitzHugh-Nagumo 神经元系统和两个几乎相同的单向耦合混沌振荡器系统中展示了这种预测和预防策略的有效性。

利用储层计算机预测和防止极端事件

Using reservoir computer to predict and prevent extreme events

Complex spatiotemporal patterns, called chimera states, consist of coexisting
coherent and incoherent domains and can be observed in networks of coupled
oscillators. The interplay of synchrony and asynchrony in complex brain
networks is an important aspect in studies of both brain function and disease.
We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex
networks motivated by its potential application to epileptology and epilepsy
surgery. We compare two topologies: an empirical structural neural connectivity
derived from diffusion-weighted magnetic resonance imaging and a mathematically
constructed network with modular fractal connectivity. We analyse the
properties of chimeras and partially synchronized states, and obtain regions of
their stability in the parameter planes. Furthermore, we qualitatively simulate
the dynamics of epileptic seizures and study the influence of the removal of
nodes on the network synchronizability, which can be useful for applications to
epileptic surgery.

本文研究耦合振荡器网络的混沌现象，利用基于扩散加权磁共振成像的实验结构神经连接和基于模块化分形连接的数学构造网络比较，分析混沌现象的性质，模拟癫痫强直性发作动力学并研究节点切除对网络同步性的影响，有助于癫痫手术应用。