Neural network-based surrogate model for 3D edge-plasma transport in the standard configuration of W7-X

This paper presents a neural‐network surrogate model for Wendelstein 7-X (W7-X) edge transport simulations, trained on an EMC3-EIRENE dataset that spans nearly the entire operating-parameter space currently explored in the W7-X standard configuration. The model uses an autoencoder to compress EMC3-EIRENE outputs into a low-dimensional latent space representation, then a neural regressor maps EMC3-EIRENE input parameters to those latent vectors, and finally both components are fine-tuned together to predict outputs directly from inputs. In benchmark tests, the improved surrogate outperforms a traditional multilayer perceptron model, most notably in predicting detached regime. Leave‐one‐value‐out evaluation indicates high accuracy within the interpolation domain, with minor degradation when extrapolating. Relative to full EMC3‐EIRENE runs, the surrogate provides over a speedup, enabling large-scale parameter scans or real-time feedback control based on 3D transport simulations to become feasible in the future.