A neural network-based method for input parameter optimization of edge transport modeling utilizing experimental diagnostics

Abstract A neural network–based method is developed to fast optimize EMC3-EIRENE input parameters, enabling EMC3-EIRENE to produce synthetic data that closely match experimental measurements on Wendelstein 7-X (W7-X). Initially, an EMC3-EIRENE simulation database covering a range of key input parameters is generated. Trained on this database, a feed-forward neural network (FNN) surrogate model efficiently maps EMC3-EIRENE input parameters to synthetic signals corresponding to experimentally observed physical quantities. Subsequently, the trained surrogate model is incorporated into a Bayesian inference framework with Dynamic Nested Sampling to infer posterior distributions of the EMC3-EIRENE input parameters. In this step, the FNN-predicted synthetic data are compared with the experimental data, and the likelihood function explicitly accounts for the measurement uncertainties of the selected diagnostics. EMC3-EIRENE simulations using the maximum a posteriori (MAP) estimates derived from these posterior distributions reproduce experimental measurements with satisfactory accuracy. This neural network-based method significantly reduces computational costs and the need for manual parameter tuning, and it can be generalized to other similar modeling codes.