Biogeochemical models increasingly consider the microbial control of carbon cycling in soil.
The major current challenge is to validate mechanistic descriptions of microbial processes …

With the increasing penetrations of wind power in power systems, periodical calibration of
the large-scale Wind Farm (WF) model is crucial to maintaining high-quality model …

This paper describes a data-driven symbolic regression identification method tailored to
power systems and demonstrated on different synchronous generator (SG) models. In this …

In this paper, we consider the problem of quantifying parametric uncertainty in classical
empirical interatomic potentials (IPs) using both Bayesian (Markov Chain Monte Carlo) and …

The paper describes a data-driven system identification method tailored to power systems
and demonstrated on models of synchronous generators (SGs). In this work, we extend the …

With the high penetration of large-scale wind farms (WFs), accurate grid-connected WF
models are crucial to be developed for studying the stability of power grids. Due to the large …

In this two-part paper, we investigate the channel estimation for massive multiple-input
multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In Part I …

Complex nonlinear models are typically ill conditioned or sloppy; their predictions are
significantly affected by only a small subset of parameter combinations, and parameters are …

The paper describes a manifold learning-based algorithm for big data classification and
reduction, as well as parameter identification in real-time operation of a power system. Both …

The time-varying parameter identification of load models has attracted broad attention when
large amounts of intermittent distributed generations (DGs) and stochastic loads are …