While popular AI models such as ChatGPT are trained on language or photographs, new models created by researchers from the ...

How can quantum field theories be best formulated on a lattice to optimally simulate them on a computer? The answer comes ...

Innovation flourishes not because people collaborate more broadly but because the right people fall into productive orbit ...

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 ...

This repository allows you to solve forward and inverse problems related to partial differential equations (PDEs) using finite basis physics-informed neural networks (FBPINNs). To improve the ...

A long-standing law of thermodynamics turns out to have a loophole at the smallest scales. Researchers have shown that quantum engines made of correlated particles can exceed the traditional ...

But only in the last 70 years have we known for certain they were there. In 1956, physicists Clyde Cowan and Frederick Reines ...

Quantum mechanics is rich with paradoxes and contradictions. It describes a microscopic world in which particles exist in a ...

Abstract: Physics-informed neural networks (PINNs) have been successfully applied in electromagnetism (EM) for the solution of direct problems. However, since PINNs typically do not take system ...

Abstract: This paper proposes a new Multi-Objective Plasma Generation Optimization (MOPGO) algorithm, and its non-dominated sorting mechanism is investigated for numerous challenging real-world ...

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 ...