Symplectic physics-embedded learning via Lie groups Hamiltonian formulation for serial manipulator dynamics prediction
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-17935-w
Abstract: Accurate dynamic modeling is critical for advanced robotic control, yet conventional methods struggle with manipulator nonlinear complexity. While Hamiltonian neural networks leveraging Lie group symmetries improve physical consistency of the network, existing methods overlook key… read more here.
Keywords: symplectic physics; manipulator; lie; physics embedded ... See more keywords
A Fourier Neural Operator Enhanced Physics-Embedded Iterative Learning Solver for Electromagnetic Scattering Analysis
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Antennas and Wireless Propagation Letters"
DOI: 10.1109/lawp.2025.3552500
Abstract: In this letter, an efficient and accurate physics-embedded Fletcher–Reeves conjugate gradient (FRCG) projection method, termed conjugate gradient-based Fourier neural operator (CGFNO), is proposed to solve the electromagnetic scattering problems iteratively. As a neuralnetwork, the Fourier… read more here.
Keywords: electromagnetic scattering; fourier neural; neural operator; physics embedded ... See more keywords