In addition, the article explores the use of machine learning for real-time profile control in tokamaks, a type of fusion device. The authors are developing a model that can generate predictions in under 100 microseconds using only real-time diagnostics, which could make the process of finding a successful actuator path more efficient. They also discuss a multi-institutional research project focused on machine learning for real-time fusion plasma behavior monitoring using high-resolution diagnostics. The authors are developing several machine learning models to classify different modes in a dataset of approximately 1000 discharges.
Key takeaways:
- The researchers have developed a groundbreaking machine learning methodology, Diag2Diag, that generates super-resolution data to understand complex systems governed by multi-spatial and multi-temporal physics scales. This methodology is particularly useful in studying the Edge Localized Mode (ELM), a plasma instability in fusion plasmas.
- They are also working on a machine-learning model for 'model-predictive control' in tokamaks, which can predict the evolution of plasma state based on various actuator settings, making the process of finding a successful actuator path more efficient.
- The Plasma Control Group is leading a research project to use machine learning for real-time fusion plasma behavior monitoring using high resolution diagnostics. They have developed ML models that can detect and classify instabilities in the core of plasma.
- Machine learning-based approaches are being used to create synthetic diagnostics in fusion research devices and reactors, which can help circumvent limitations in diagnostic availability and scope in future fusion reactors.