Physics-Informed Planning
Cross-source consensus on Physics-Informed Planning from 1 sources and 6 claims.
1 sources · 6 claims
Uses
How it works
Benefits
Highlighted claims
- Physics-informed acquisition ranks candidate points by expected information gain per unit wall-clock time. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- AIC-derived weights are used as a real-time proxy for model evidence during Hamiltonian discrimination. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- The local posterior is approximated as Gaussian, and expected information gain is computed with a Laplace-style expression for fast in-loop ranking. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- When the Hamiltonian family is known, TAS-AI reached the target RMS threshold faster than the competing methods in the representative refinement run. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- Physics-informed planning becomes useful after a plausible signal region and model family are available. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- MCMC is reserved for batch boundaries or offline validation when local diagnostics fail. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy