Physics-informed neural networks (PINNs) represent a burgeoning paradigm in computational science, whereby deep learning frameworks are augmented with explicit physical laws to solve both forward and ...

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 ...

Porous media play a critical role in various industrial fields due to their complex pore networks and considerable specific surface areas. The transport and reaction phenomena within porous media are ...

Understand Local Response Normalization (LRN) in deep learning: what it is, why it was introduced, and how it works in ...

Engineers have uncovered an unexpected pattern in how neural networks -- the systems leading today's AI revolution -- learn, suggesting an answer to one of the most important unanswered questions in ...

Parth is a technology analyst and writer specializing in the comprehensive review and feature exploration of the Android ecosystem. His work is distinguished by its meticulous focus on flagship ...

The 2024 Nobel Prize in Physics has been awarded to scientists John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural ...

Ambuj Tewari receives funding from the NSF. If your jaw dropped as you watched the latest AI-generated video, your bank balance was saved from criminals by a fraud detection system, or your day was ...

On Thursday, Google and the Computer History Museum (CHM) jointly released the source code for AlexNet, the convolutional neural network (CNN) that many credit with transforming the AI field in 2012 ...

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...