EurekAlert!

Transfer learning-enhanced physics-informed neural network (TLE-PINN): A breakthrough in melt pool prediction for laser melting

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...
Quanta Magazine

Novel Architecture Makes Neural Networks More Understandable

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...
Scientific Research Publishing

UWB NLOS Signal Recognition Based on Deep Learning ()

However, in indoor environments, non-line-of-sight (NLOS) signals significantly degrade the ranging performance of UWB ...
Scientific Research Publishing

A Spatio-Temporal Prediction Model for Wall Turbulence Based on Hybrid Neural Networks ()

The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and ...
EurekAlert!

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery

As the world grapples with the energy crisis and environmental concerns, the focus on renewable energy sources has intensified. Lithium-ion batteries, with their high energy density and low pollution, ...
MIT Technology Review

A new way to build neural networks could make AI more understandable

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.
Scientific American

An Alternative to Conventional Neural Networks Could Help Reveal What AI Is Doing behind the Scenes

ChatGPT has triggered an onslaught of artificial intelligence hype. The arrival of OpenAI’s large-language-model-powered (LLM-powered) chatbot forced leading tech companies to follow suit with similar ...