Overcoming error-in-variable problem in data-driven model discovery by orthogonal distance regression
用正交距离回归解决数据驱动模型发现中的变量测量误差
Despite the recent proliferation of machine learning methods like SINDy that promise automatic discovery of governing equations from time-series data, there remain significant challenges to discovering models from noisy datasets. One reason is that the linear regression underlying these methods assumes that all noise resides in the training target (the regressand), which is the time derivative, whereas the measurement noise is in the states (the regressors). Recent methods…