利用下一代储层计算推断未知动力学分量：从混沌系统到气候数据

中文摘要 AI 辅助

我们研究了下一代储层计算（NGRC）作为一种数据驱动方法，用于推断动力系统中未观测到的分量。我们使用Lorenz和Rössler系统将NGRC与传统储层计算（RC）进行比较，在这两个系统中，从给定的一个分量推断出两个未知分量。对于这两个系统，NGRC在需要更少的训练数据和更少的计算时间的情况下取得了准确的结果。我们发现了NGRC所需的时间延迟步数与时间分辨率之间的反比关系，这表明延迟间隔所覆盖的物理时间跨度是决定所需延迟步数的重要因素。最后，我们将NGRC应用于ENSO（厄尔尼诺-南方涛动）的观测气候数据，并从其余变量中推断出一个可观测变量。尽管真实世界数据存在噪声和复杂性，NGRC仍显示出有前景的结果。我们的发现证明了NGRC在受控动力系统和真实世界数据中高效推断未知分量的潜力。

英文摘要

We investigate next generation reservoir computing (NGRC) as a data-driven approach for inferring unseen components of dynamical systems. We compare NGRC with traditional reservoir computing (RC) using the Lorenz and Rössler system, where two unknown components are inferred from one given component. For both systems, NGRC achieves accurate results while requiring fewer training data and less computational time than RC. We identified an inverse proportional behavior between the number of time-delayed steps needed for NGRC and the temporal resolution, indicating that the physical time span covered by the delay interval is an important factor in determining the required number of delayed steps. Finally, we apply NGRC to the observational climate data of ENSO (El Niño--Southern Oscillation) and infer one observable from the remaining variables. Despite the noise and complexity of the real-world data, the NGRC shows promising results. Our findings demonstrate the potential of NGRC for efficient inference of unseen components in both controlled dynamical systems and real-world data.

发表机构

• University College Cork（科克大学）
• Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）

机构由 AI 辅助整理，请以论文原文为准。