Heteroscedastic Neural Surrogate Modeling for Robust and Rapid Bayesian Inference in Fusion Plasma Diagnostics
TL;DR
This work proposes a neural-network-based probabilistic surrogate framework for rapid and robust MCMC inference that achieves>1500x acceleration over exact physical forward models, while simultaneously reducing inference error (RMSE) by>20% compared to standard homoscedastic neural baselines, offering a highly promising paradigm for real-time physical analysis.
Abstract
Bayesian inference via Markov Chain Monte Carlo (MCMC) provides effective parameter estimation, but its real-time application in complex physical systems is hindered by heavy computational bottlenecks and extreme sensitivity to statistical noise. We address this by proposing a neural-network-based probabilistic surrogate framework for rapid and robust MCMC inference. Using fusion plasma Thomson scattering diagnostics as a challenging, noise-dominated testbed, our approach employs a dual-head architecture to simultaneously estimate the expected physical emission spectrum and the channel-wise intrinsic measurement noise variance. By optimizing a Gaussian Negative Log-Likelihood (GNLL) objective, the learned aleatoric uncertainty dynamically buffers the sampler against pathological shot noise. Evaluations demonstrate that this surrogate framework achieves>1500x acceleration over exact physical forward models, while simultaneously reducing inference error (RMSE) by>20% compared to standard homoscedastic neural baselines, offering a highly promising paradigm for real-time physical analysis.