Digital-Twin Benchmarks
Cross-source consensus on Digital-Twin Benchmarks from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Risks & contraindications
Evidence quality
Highlighted claims
- The simulations use realistic thermal-neutron triple-axis assumptions, including fixed final energy, collimations, PG optics, kinematic constraints, and Cooper-Nathans-derived broadening. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- The benchmarks use simplified square-lattice models with at most three exchange parameters to isolate the control architecture. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- The simulations include realistic broadening, kinematics, motion costs, and noise, but they are not live instrument deployments. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- Synthetic data combine Poisson counting noise with a systematic intensity-dependent floor. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- Resolution treatment broadens models only in energy rather than using full four-dimensional convolution. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy