Next generation reservoir computing infers missing parts of complex systems
Inference of Unknown Dynamical Components Using Next Generation Reservoir Computing: From Chaotic Systems to Climate Data
Machine Learning
Summary
Many systems, like the weather or climate, have parts we cannot directly observe, making prediction challenging. The authors used a method called next generation reservoir computing to guess missing parts of these systems from available data. They tested this on well-known chaotic systems and real climate data, showing it works well using less data and time than older methods. This approach might help better understand and predict complex natural phenomena by filling in unknown details.
What this means in practice
- •For climate modelers: Infer missing climate variables from observed data to improve model completeness and accuracy for phenomena like El Niño.
- •For control systems engineers: Reconstruct unmeasured states in nonlinear dynamical systems from limited sensor data for better system monitoring or control.
Authors
Jule Budnick, Andrew Keane, Serhiy Yanchuk
Abstract
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.