NGRC 从单一观测推断混沌系统未知分量，验证扩展至 ENSO 气候数据
Inference of Unknown Dynamical Components Using Next Generation Reservoir Computing: From Chaotic Systems to Climate Data
NGRC 用更少数据就能从 Lorenz 系统一个变量推出另外两个，比传统 RC 省时省算力，还在真实 ENSO 气候数据上跑通了
一项 arXiv 研究用下一代储备池计算（NGRC）推断动力系统中未观测到的分量。在 Lorenz 和 Rössler 混沌系统上，从单一给定分量推断两个未知分量时，NGRC 比传统 RC 需要更少训练数据和更短计算时间。研究还发现 NGRC 所需时延步数与时间分辨率成反比，时延区间覆盖的物理时间跨度是决定步数的关键因素。团队将 NGRC 应用于 ENSO（El Niño–Southern Oscillation）观测气候数据，从其余变量推断单个可观测量，在有噪声的真实数据中仍得到可用结果。
Inference of Unknown Dynamical Components Using Next Generation Reservoir Computing: From Chaotic Systems to Climate Data
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.