Ultra‑robust machine‑learning models run stable molecular simulations at extreme temperatures

Researchers at The University of Manchester have created a physics‑informed machine‑learning model that can run molecular ...

Nvidia-backed ThinkLabs AI raises $28 million to tackle a growing power grid crunch

ThinkLabs AI has raised $28 million with backing from NVIDIA and energy investors to help utilities modernize power-grid ...
Science Daily

This new “phonon laser” could measure gravity more precisely than ever before

Scientists have taken lasers beyond light and into the realm of sound, creating a breakthrough “phonon laser” that ...
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 ...
IEEE

Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities

Abstract: Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such knowledge ...
Scientific Research Publishing

Predicting Ship Propeller Speed with Multi-Source Data Fusion and Physics-Informed LightGBM: A Novel Correction Framework ()

1 COSCO Shipping Technology Co., Ltd., Shanghai, China. 2 State Key Laboratory of Ocean Engineering, Department of Transportation Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong ...
GitHub

Physics-Informed Neural Network for Motor Thermal Prediction

Accurate long-term temperature prediction Consistency with energy conservation and thermal dynamics Reduced need for labeled data Robust generalization across operating conditions Electric motor ...