Journal of Theoretical, Experimental, and Applied Physics
Physics-Informed Representation Learning: Lie Group Variational Autoencoders and Noether Networks Abstract
This paper explores the role of symmetries and invariants in machine learning models for physics, focusing on two approaches: Lie Group Variational Autoencoders (Lie-VAE) and Noether Networks. We present theoretical foundations, architectures, and a comparative analysis of their applications to dynamical systems and time series.
Journal key Highlights
Accelerator physics Acoustics Aerospace engineering Agrophysics Analog electronics Applied Quantum systems Artificial intelligence. Astrodynamics Astrophysics Atomic force microscopy Ballistics Biophotonics Biophysics. Brain-computer interfacing Cavity optomechanics Chemical engineering. Communication physics Computational physics Condensed matter physics. Control engineering. Control theory Differentiable programming Digital electronics Econophysics Electrical engineering. Electromagnetic propulsion Electronics engineering. Engineering physics. Experimental physics Fiber optics Fluid dynamics. Force microscopy Fuel cell technology Geophysics Health physics Hydrogen generation Laser physics Lidar Magnetic resonance imaging. Materials physics. Materials science & engineering Medical imaging and diagnosis Medical physics. Metamaterials Metrological physics Microfluidics Nanoelectronics. Nanomaterials. Nondestructive testing Nuclear engineering Nuclear fission reactors Nuclear fusion reactors Nuclear technology Optical engineering Optics Optoelectronics Petrophysics Photonic crystals Photonics Photovoltaics Plasma physics Polymers Power electronics Power engineering Quantum biochemistry Quantum computing Quantum cryptography. Quantum dots. Quantum electronics Quantum sensing Radar Radiation dosimetry Radiation therapy Renewable energy. Scanning electron microscopy Scanning probe microscopy Scanning tunneling microscopy Scientific computing Scientific instrumentation Semiconductor physics. Solid state physics. Sonar Space physics. Spectroscopy. Spin dynamics Spintronics Stealth technology Superconductors. Thin films Transmission electron microscopy Vehicle dynamics.
