Hosted on MSN

Harvard physicists create physics-inspired model to decode AI learning

Harvard University physicists have developed a simplified, physics-inspired mathematical model to better understand how neural networks learn. Published in the Journal of Statistical Mechanics, the ...
IEEE

A 2D Physics-Informed Neural Network-Based Inverse-Design Method of Antenna Superstrate for Radiation and Scattering Problems

Abstract: Based on unsupervised physics-informed neural network (PINN) framework, a two-dimensional inverse-design method for antenna superstrate is proposed, which can simultaneously realize the ...
Hosted on MSN

Harvard physicists unveil model to decode neural networks

Harvard University physicists have developed a simplified, physics-based mathematical model to better understand how neural ...
IEEE

Physics-Informed Neural Networks and their Implementation in MATLAB

Abstract: An analysis was made of physics-informed neural networks used to solve partial differential equations. The prospects for the implementation of physics-informed neural networks in the MATLAB ...
justthenews

Singham uses extensive CCP-aligned network in China as he finances global Marxist influence efforts

A Just the News investigation has detailed how a wealthy Marxist activist best known for the funding of a global financial network both inside the U.S. and around the world has extensive ties to ...
Frontiers

Implementing physics-informed neural networks with deep learning for differential equations

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...
GitHub

Space Correlation Constrained Physics Informed Neural Network (SC-PINN) for Seismic Tomography

This repository contains the source code for the paper "Space Correlation Constrained Physics Informed Neural Network for Seismic Tomography", accepted by JGR: Machine Learning and Computation on ...
Scientific Research Publishing

Liu, H., Ding, X., Liu, J., Zhang, Y., Zhang, B., Li, E., et al. (2025) A Physics-Informed Neural Network Model for Combined High and Low Cycle Fatigue Life Prediction ...

ABSTRACT: Rubber is widely used in automotive vibration isolation systems due to its excellent mechanical properties and durability. However, elastomeric support components tend to experience ...
Frontiers

Bridging computational power and environmental challenges: a perspective on neural network predictive models for environmental engineering

The escalating frequency and severity of extreme environmental events underscores the critical need for a paradigm shift from reactive to proactive management strategies. This perspective article ...