This study introduces a physics-regularized neural network (PRNN) as a computational approach to predict silicon carbide’s (SiC) swelling under irradiation, particularly at high temperatures. The PRNN ...
Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...
ANKARA, TURKIYE - OCTOBER 8: An infographic titled "2024 Nobel Prize" created in Ankara, Turkiye on October 8, 2024. 2024 Nobel Prize in physics awarded to John J. Hopfield, Geoffrey E. Hinton for ...
This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown parameters in a “mean ...
Researchers in Sweden have developed a machine-learning approach that embeds the laws of physics directly into neural ...
Physics AI engineering simulation tools reached production at General Motors this week, cutting a two-week aerodynamics cycle ...
The human brain, with its billions of interconnected neurons giving rise to consciousness, is generally considered the most powerful and flexible computer in the known universe. Yet for decades ...
Autodesk's Mike Haley takes a closer look at what Autodesk is calling the next stage in 3D design "neural CAD" AI foundation ...