Governing equations are foundations for modelling, predicting, and understanding the Earth
system. The Earth system is undergoing rapid change, and the conventional approaches for …

In this study, facing the challenges on model reconstruction for multi-attractor nonlinear
systems, the data generation and sparse regression processes in sparse identification …

Weak form equation learning and surrogate modeling has proven to be computationally
efficient and robust to measurement noise in a wide range of applications including ODE …

Recent work in data-driven modeling has demonstrated that a weak formulation of model
equations enhances the noise robustness of a wide range of computational methods. In this …

We present a data-driven and interpretable approach for reducing the dimensionality of
chaotic systems using spectral submanifolds (SSMs). Emanating from fixed points or …

Chaos and unpredictability are traditionally synonymous, yet large-scale machine-learning
methods recently have demonstrated a surprising ability to forecast chaotic systems well …

The sparse identification of nonlinear dynamics (SINDy) has been established as an
effective technique to produce interpretable models of dynamical systems from time …

Abstract Time Series Classification (TSC) is essential in fields like medicine, environmental
science, and finance, enabling tasks such as disease diagnosis, anomaly detection, and …

Identifying dynamical systems characterized by nonlinear parameters presents significant
challenges in deriving mathematical models that enhance understanding of physics …

In this paper, we propose a sparse modeling method for automatically creating a surrogate
model for nonlinear time-variant systems from a very small number of time series data with …