University of Utah engineers encode partial differential equations in light and feed them into newly designed optical neural engine, or ONE, to accelerate machine learning.

A neural network-based method for solving a class of high-order nonlinear partial differential equations* Publisher: IEEE Cite This PDF ...

Method Using a Density–Energy State Function with a Reference Equation of State for Fluid-Dynamics Simulation of Vapor–Liquid–Solid Carbon Dioxide. Industrial & Engineering Chemistry Research 2013, 52 ...

Carboxylic acids are ubiquitous in bioactive organic molecules and readily available chemical building blocks. Carboxylic ...

Researchers at Georgia Tech's School of Chemical and Biomolecular Engineering (ChBE) have developed a promising approach for ...

Oxygen evolution is considered one of the most energy-intensive steps in water electrolysis and is therefore a key factor for ...

A state-of-the-art deep domain decomposition method (D3M) based on the variational principle is proposed for partial differential equations (PDEs). The solution of PDEs can be formulated as the ...

Food sensitivity tests promise to reveal your trigger foods. But due to false positives and unreliable methods, they're not advised. Here's what to consider instead.

For example, it is shown how the matrix formalism is useful to treat ray Optics, atomic systems evolution, QED, QCD and Feynman diagrams. The methods presented here are simple but rigorous. They allow ...

🔥 Physics-Informed Neural Network — 1D Heat Equation This project solves the 1D heat equation using two approaches: Physics-Informed Neural Networks (PINNs) — a neural network that embeds the physics ...

Recent studies have demonstrated the applicability of the “single particle” mode in laser ablation–inductively coupled plasma–mass spectrometry to map and size particles simultaneously. The transport ...