New low-power robot works using echolocation and could find applications in search and rescue missions in difficult-to-access ...
Physicists are rethinking how to detect elusive particles like neutrinos by combining existing technologies in unconventional ...
Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving ...
Abstract: A physics-informed neural network (PINN) method for modeling electromagnetic beam propagation problems is presented. A scaling formulation for the paraxial wave equation is proposed to train ...
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: Camera model calibration establishes an accurate mapping between the 3-D physical world and 2-D images, enhancing the precision and reliability of vision measurements. Conventional ...
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 ...
Accurate joint kinematics estimation is essential for understanding human movement and supporting biomechanical applications. Although optical motion capture systems are accurate, their high cost, ...
πMRF (Physics-informed implicit neural MRF) is a physics-informed unsupervised framework for accurate quantitative parameter mapping via global spatio-temporal inversion. piMRF/ ├── main.py # Runnable ...
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