Princeton Plasma Physics Laboratory
Machine Learning Applications to Predictive Studies of Disruptions in Tokamaks
Kyle Felker, Princeton University
Practicum Year: 2016
Practicum Supervisor: William Tang, , Theory and Computation, Princeton Plasma Physics Laboratory
My practicum broadly approached the emerging collaboration of machine learning and fusion science at PPPL under Dr. William Tang. In particular, supervised machine learning methods have been used to successfully predict the onset of “disruptions” during the operation of tokamak reactors. Disruptive events must be avoided or mitigated to enable fusion energy production and to prevent damage to the reactor. Conventional first-principles calculations are unable to accurately model the complex dynamics that lead to disruptions in real-time. Using empirical signal data from the Joint European Torus (JET), we seek to apply new modern machine learning methods to the disruption problem, improve the predictive capabilities of the methods via addition of new signal data, and predict disruptions on a tokamak using algorithms trained on a different tokamak’s data. More broadly, we hope to generate promising results to encourage the application of machine learning methods within the fusion science community and establish an open data-sharing collaboration with the major tokamaks.
Disruption Forecasting in Tokamak Fusion Plasmas using Deep Recurrent Neural Networks
Julian Kates-Harbeck, Harvard University
Practicum Year: 2016
Practicum Supervisor: William Tang, Chief Scientist, Princeton Plasma Physics Laboratory, Princeton Plasma Physics Laboratory
The prediction and avoidance of disruptions in tokamak fusion plasmas represents a key challenge on the way to stable energy production from nuclear fusion. Using diagnostic data from experimental runs with both disruptive and non-disruptive outcomes, we trained a deep recurrent neural network to predict the onset of disruptions. The algorithm achieves competitive performance on data from the JET tokamak. To deal with very large amounts of data and the need for iterative hyperparameter tuning, we also introduced a distributed training algorithm that runs on MPI clusters of GPU nodes and provides strong linear runtime scaling.
Energetic particle affects on tokomak stability using continuum kinetic method implemented on many-core machines
Noah Reddell, University of Washington
Practicum Year: 2011
Practicum Supervisor: Guo-Yong Fu, Principle Research Physicist, Theory Department, Princeton Plasma Physics Laboratory
We studied the nonlinear physics of energetic particle-induced geodesic acoustic mode (EGAM) in tokomak plasma confinement. In this phenomenon, fast ions are present that have a velocity distribution far from Maxwellian. To capture the affect of the fast ions, a continuum kinetic approach is used where we keep track of the distribution of fast ions in both position and velocity space. This adds extra dimensionality to the problem and thus more computational complexity. We developed a code to solve this problem that is well suited for many-core machines such as GPUs.
Effects of Noise and Attention on Memory Learning
Armen Kherlopian, Cornell University
Practicum Year: 2009
Practicum Supervisor: Harry Mynick and Neil Pomphrey, Principal Research Physicist, Princeton University - Theory Department, Princeton Plasma Physics Laboratory
Biological sensory systems are adept at performing a wide array of complex computational tasks related to pattern recognition. In the case of vision, organisms must track objects through space, make comparisons against memory, and learn from past experiences. Understanding how these tasks are completed is important both for gaining insight on natural systems and for developing methods for trend finding. Working toward these goals we explored the behavior of an artificial intelligence system called Thinking Machine 2. We determined that the impact of attention and noise on memory learning varied for different object classes as a function of feature similarity.
Advanced Simulations of Plasma Microturbulence
Hal Finkel, Yale University
Practicum Year: 2008
Practicum Supervisor: William Tang, Professor, Princeton Plasma Physics Laboratory, Princeton Plasma Physics Laboratory
Using the Gyrokinetic Toroidal Code (GTC), which is PPPL's flagship code for studying plasma microturbulence in magnetically-confined high-temperature plasmas, running on DOE's Intrepid BG/P system, conduct a systematic study of the long-time evolution of plasma microturbulence including both collisionless dissipation from the ion temperature gradient (ITG) instability and collisonal dissipation from classical Coulomb collisional dynamics.
Gyrokinetic Simulations on the Cray T3D
Jeremy Kepner, Princeton University
Practicum Year: 1995
Practicum Supervisor: Dr. Scott Parker, , Theory Division, Princeton Plasma Physics Laboratory
I assisted in the development of a massively parallel code for simulating Tokamaks.
Users Manual for the TSC Code
William Daughton, Massachusetts Institute of Technology
Practicum Year: 1993
Practicum Supervisor: Dr. Linda Sugiyama, , Physics of Thermonuclear Plasma, Princeton Plasma Physics Laboratory
The TSC code (Tokamak Simulation Code) was developed by scientists at the Plasma Phyiscs Laboratory in 1986 to model the transport and stability of axisymmetric plasmas in tokamak reactors. The code has since been modified by other scientist around the world and has become a useful design tool for new reactors. The code is difficult to use and the existing documentation was incomplete and outdated. My assignment was to learn the physics and numerical methods used in the model and to write a comprehensive users manual.