Nature

Deep Learning and Neural Network Approximations

Deep learning has emerged as a transformative paradigm in modern computational science, leveraging neural networks to approximate complex functions across a variety of domains. Central to this ...
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 ...
EurekAlert!

A novel deep learning model for medical image segmentation with convolutional neural network and transformer

Accurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that ...
Quanta Magazine

Improving Deep Learning With a Little Help From Physics

Rose Yu has a plan for how to make AI better, faster and smarter — and it’s already yielding results. When she was 10 years old, Rose Yu got a birthday present that would change her life — and, ...
Ars Technica

How a stubborn computer scientist accidentally launched the deep learning boom

During my first semester as a computer science graduate student at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the semester, there was a lecture about neural networks. This ...