ML-Assisted Diagnostics Overview
Across diagnostics, machine learning inverts sensor signals into plasma quantities, fuses multiple instruments, and flags faults faster than manual analysis.
Diagnostics as inverse problems
Most plasma diagnostics do not measure the quantity of interest directly. They record a signal that depends on the plasma state through a forward model, and the analyst must invert that model to recover temperature, density, current, or radiation. Machine learning offers fast, robust inversions across the whole diagnostic suite.
Common pattern
- Build a faithful forward model mapping plasma state to synthetic signals
- Generate training data by sampling plausible plasma states
- Train a network to invert signals back to plasma quantities
- Attach uncertainty estimates and validate against classical analysis
This pattern recurs for Thomson scattering, interferometry, bolometric tomography, spectroscopy, and magnetic reconstruction. The learned inverse runs in milliseconds, enabling near-real-time state estimation that manual fitting cannot match.
Data fusion
Individual diagnostics are noisy and incomplete. Combining them under a common physics-based model yields a more accurate and self-consistent plasma state. Machine learning accelerates the forward models and provides priors that make multi-diagnostic fusion feasible within a control cycle.
Trust and validation
Because inversions are ill-posed, calibrated uncertainty is not optional. Ensembles and Bayesian methods indicate when a signal is uninformative, and systematic errors in the forward model are the dominant risk. Learned inversions are validated against established analysis before being relied on.
For a machine still in design, such as the Hyperion breeder concept, diagnostic pipelines are prepared and tested against simulated data ahead of construction beginning in Q2 2027. They operate on modeled signals until hardware exists, and every inferred quantity is treated as a computational estimate to be confirmed by measurement.