This paper presents a generalized approach to identify the structure of governing nonlinear
equations of motion from the time history of state variables and control functions. An integral …

Numerous researches have shown that there are three main challenges in data-driven
model identification methods: high-dimensional measurements, system complexity and …

Advances in machine learning and deep neural networks have enabled complex
engineering tasks like image recognition, anomaly detection, regression, and multi-objective …

A crucial challenge encountered in diverse areas of engineering applications involves
speculating the governing equations based upon partial observations. On this basis, a …

In recent years, identification of nonlinear dynamical systems from data has become
increasingly popular. Sparse regression approaches, such as sparse identification of …

Modern societies have an abundance of data yet good system models are rare.
Unfortunately, many of the current system identification and machine learning techniques fail …

This work proposes an iterative sparse-regularized regression method to recover governing
equations of nonlinear dynamical systems from noisy state measurements. The method is …

Most works related to the identification of mathematical nonlinear systems suggest that such
approaches can always be directly applied to any nonlinear system. This misconception is …

This work aims to provide a unified and automatic framework to discover governing
equations underlying a dynamical system from data measurements. In an appropriate basis …

The ability to discover physical laws and governing equations from data is one of
humankind's greatest intellectual achievements. A quantitative understanding of dynamic …