Approximating nonlinear differential equations using a neural network provides a robust and
efficient tool for various scientific computing tasks, including real-time predictions, inverse …

Sparse random mode decomposition (SRMD) is a decomposition approach established by
combining sparse random feature model with clustering algorithm. It is not subject to the …

Sparse random mode decomposition (SRMD) is an emerging signal decomposition method
for analyzing time series data. However, SRMD is unable to adaptively select suitable …

In models of opinion dynamics, many parameters—either in the form of constants or in the
form of functions—play a critical role in describing, calibrating, and forecasting how opinions …

We present a comprehensive examination of learning methodologies employed for the
structural identification of dynamical systems. These techniques are designed to elucidate …

Designing privacy-preserving machine learning algorithms has received great attention in
recent years, especially in the setting when the data contains sensitive information …

We present a novel Bayesian learning framework for interacting particle systems with
discrete latent states, addressing the challenge of inferring dynamics from partial, noisy …