Understand vector addition through real physics problem examples. This video explains how vectors combine using clear diagrams and practical applications, making the concept easy to follow for ...

Adobe says that this ensures Firefly Foundry models enable “responsible AI adoption across every stage of production” from ...

Master vector derivatives in polar coordinates with this clear physics tutorial! Learn step-by-step how to compute derivatives, understand radial and angular components, and apply them to mechanics ...

Axions are hypothetical light particles that could solve two different physics problems, as they could explain why some nuclear interactions don't violate time symmetry and are also promising dark ...

Physics is the search for and application of rules that can help us understand and predict the world around us. Central to physics are ideas such as energy, mass, particles and waves. Physics attempts ...

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Atomic and molecular physics it the study of the properties, dynamics and interactions of the basic (but not fundamental) building blocks of matter. A crucial component of this is understanding the ...

Abstract: In recent years, deep learning-based methods have been introduced for solving inverse scattering problems (ISPs), but most of them heavily rely on large training datasets and suffer from ...

Abstract: Ising machines have recently attracted attention as a new computational method for efficiently solving combinatorial optimization problems. NEC Vector Annealing (VA), working on vector ...

John Clarke, Michel Devoret and John Martinis won the 2025 Nobel Prize in Physics for "experiments that revealed quantum physics in action", the Royal Swedish Academy of Sciences, the award-giving ...

Initially the intent is to include ~1-page pdf files, each treating a specific problem in plasma physics (e.g., landau damping, two-stream instability, etc.). Eventually we are going to create codes ...

This repository implements advanced physics-driven deep learning frameworks for solving partial differential equations (PDEs) and related inverse problems. These methods leverage deep neural networks ...