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2D linear drag: Analytical and numerical solutions compared

Explore 2D linear drag by comparing analytical and numerical solutions in this clear and engaging physics tutorial. In this video, we break down the mathematics behind linear drag forces, demonstrate ...

How machine learning helps MEMS actuators move in perfect lines

Microelectromechanical systems (MEMS) electrothermal actuators are widely used in applications ranging from micro-optics and ...

How Indigenous ideas about nonlinear time can help us navigate ecological crises

For us, nonlinear time finds natural expression in a metaphor: the koru, or unfolding fern frond. Researcher Paula Toko King ...

Seeing the whole from a part: Revealing hidden turbulent structures from limited observations and equations

The irregular, swirling motion of fluids we call turbulence can be found everywhere, from stirring in a teacup to currents in ...
GitHub

Nonlinear Model-Predictive Control Using Koopman Operator

This document provides a detailed explanation of the MATLAB code that demonstrates the application of the Koopman operator theory for controlling a nonlinear system using Model Predictive Control (MPC ...
IEEE

A Linear Surrogate-Based Algorithm for Fitting Gaussian Mixture Functions

Abstract: Gaussian Mixture Function (GMF) is a widely utilized model for analyzing and elucidating experimental data in science and engineering, where the fitting of GMF with noisy observations is ...
IEEE

Treating the Filter Weights as Learnable Functions: An Efficient Nonlinear Filtering Framework and Its Adaptive Algorithms

Abstract: Adaptive filters, constrained by a linear filtering framework, often struggle with nonlinear modeling in complex processes. Kernel adaptive filters (KAFs) offer a promising solution by ...