IEEE

Cholesky-KalmanNet: Model-Based Deep Learning With Positive Definite Error Covariance Structure

Abstract: State estimation from noisy observations is crucial across various fields. Traditional methods such as Kalman, Extended Kalman, and Unscented Kalman Filter often struggle with nonlinearities ...
PBS

Decomposers and Scavengers

Patrice looks at the important role decomposers and scavengers play in the natural world. Patrice looks at the important role decomposers and scavengers play in the natural world. She then visits with ...
IEEE

Comprehensive Study on Zeroing Neural Network With High-Order Evolutionary Formula, Nonlinear Functions, and Variable Parameter for Time-Changing Matrix Cholesky Decomposition

Abstract: In this article, a low-order zeroing neural network (LZNN), a high-order ZNN (HZNN), and a variable-parameter ZNN (VZNN) are designed and applied to the time-changing Cholesky decomposition ...
GitHub

Modal Decomposition for Reduced Order Modelling

This Jupyter notebook demonstrates how modal decomposition methods can be used for flow feature extraction in fluid mechanics datasets. Modal decomposition techniques, such as Proper Orthogonal ...