Research Explorer

Project

Neural network-accelerated Bayesian inference of impurity transport in fusion plasmas
Code
01SC1720
Duration
01 October 2020 → 30 September 2024
Funding
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Probability theory
    • Neural, evolutionary and fuzzy computation
    • Physics of (fusion) plasmas and electric discharges
Keywords
fusion energy impurity transport Bayesian inference neural networks
 
Project description
 

Understanding and control of impurity transport in fusion plasmas is crucial on the way to abundant, clean and safe fusion energy. Using powerful Bayesian inference, accelerated by neural network surrogate models, we will estimate impurity concentrations and parameters governing transport, including accompanying uncertainties. With these tools, impurity transport will be studied in several devices, preparing for impurity control in ITER.