Sparse methods are primarily valuable for systems in which the number of non-zero entries is substantially less than the overall size of the matrix. Such situations are common in physical systems, ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...

This project is about using Physics Informed Neural Networks (PINN) to solve unsteady turbulent flows using the Navier-Stokes equations. Specifically, given sparse observations (in this case, a mere 0 ...

Thriving in an exponential world requires more than a better strategy. It demands quantum thinking, the shift from linear ...

Physicist Albert Einstein famously posited that if he only had an hour to crack a daunting problem, he'd devote 55 minutes to ...

Abstract: This letter synthesizes a gain-scheduled controller to stabilize all possible Linear Parameter-Varying (LPV) plants that are consistent with measured input/state data records. Inspired by ...

A new role for extracellular matrix remodelling in Rheumatoid Arthritis (RA) pathology has been discovered. Dynamic collagen ...

Abstract: This article studies the stabilization problem for a class of discrete-time hidden semi-Markov jump linear systems with partly unknown emission probability matrix. By virtue of the improved ...

We also prove that the two sets of Maxwell equations only depend on the non-linear elations of the conformal group of ...

Many businesses are growing sceptical of AI/ML because they fail to see strong returns or solid fundamentals. Inora Organic ...

The inspiration for this column comes not from the epic 1999 film The Matrix, as the title may suggest, but from an episode of Sean Carroll’s Mindscape podcast that I listened to over the summer. The ...

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