Abstract: This paper introduces the Differential-Game based Physics-Informed Neural Network (DG-PINN), a trajectory-prediction model that embeds a multi-agent differential-game formulation into a ...

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 ...

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 ...

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 ...

This paper explores the integration of Physics-Informed Neural Networks (PINNs) and Robot Process Automation (RPA) tools in modeling and controlling rigid robotic joint motion. PINNs, which integrate ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

As a result, the on-chip learning-based neuromorphic system achieved up to 20,000 times faster processing speed while maintaining similar interpretation accuracy to existing conventional techniques.