Autoencoders for Plasma Dimensionality Reduction
Autoencoders compress high-dimensional plasma data into compact latent representations for visualization, anomaly detection, and surrogate modeling.
Compressing plasma data
Diagnostic and simulation data are high-dimensional: many channels, many spatial points, many timesteps. An autoencoder learns to compress this data into a low-dimensional latent code and reconstruct it, discovering the small number of factors that actually vary and discarding redundancy.
How it works
The encoder maps input to a bottleneck latent vector; the decoder reconstructs the input from it. Training minimizes reconstruction error, forcing the latent code to retain the information needed to rebuild the data. The learned code is a compact, nonlinear summary, more expressive than linear principal-component analysis.
- Visualization: view discharges or profiles in a low-dimensional space
- Anomaly detection: high reconstruction error flags unusual data
- Surrogate inputs: compact latents feed downstream models
- Denoising: reconstruct clean signals from noisy inputs
Variational variants
A variational autoencoder imposes structure on the latent space so it can be sampled, enabling generation of new plausible data. This connects dimensionality reduction to generative scenario design, where sampling the latent space proposes new candidate profiles or scenarios.
Interpretation caution
Latent dimensions are learned, not physical, so they need not correspond to interpretable quantities. Techniques that encourage disentangled or physically aligned latents help, but interpretation should be validated rather than assumed. The compression is also lossy, so fine structure can be discarded.
Compact representations support monitoring and surrogate modeling for concepts like the Hyperion breeder. In Kronos design work autoencoders are trained on simulated data before hardware exists; the latents summarize modeled plasma states and are computational tools pending validation on operating hardware.