Towards fast surrogate models for interpolation of tokamak edge plasmas

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A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved modeling allowing more flexible integration of physics models in the light of extrapolations towards future fusion devices. The project encompasses various research objectives: (a) developments of surrogate model predictors for power & particle exhaust in fusion power plants; (b) assessments of surrogate models for time-dependent phenomena in the plasma-edge; (c) feasibility studies of micro–macro model discovery for plasma-facing components surface morphology & durability; and (d) enhancements of pedestal models & databases through interpolators and generators exploiting uncertainty quantification. Presented results demonstrate useful applications for machine-learning and artificial intelligence in fusion exhaust modeling schemes, enabling an unprecedented combination of both fast and accurate simulation.

Section: Development Of a Surrogate Model For Power And Particle Exhaustmentioning

confidence: 99%

Section: Development Of a Surrogate Model For Power And Particle Exhaustmentioning

confidence: 99%

Section: Development Of a Surrogate Model For Power And Particle Exhaustmentioning

confidence: 99%

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Data-driven models in fusion exhaust: AI methods and perspectives

Wiesen,

Dasbach,

Kit

et al. 2024

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“…Machine learning (ML) offers an alternative data-driven path for obtaining quick, inexpensive approximations to numerical simulations, allowing for rapid iterative modelling [6]. Within plasma physics, ML-based surrogate models have been used to provide quick approximations for a range of solutions, from emulating turbulent transport [7][8][9], estimating the Tritium breeding ratio across Tokamak designs [10] to modelling edge plasma [11,12].…”

Section: Introductionmentioning

confidence: 99%

Plasma surrogate modelling using Fourier neural operators

Gopakumar,

Pamela,

Zanisi

et al. 2024

Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma rapidly and accurately allow us to quickly iterate over design and control strategies on current Tokamak devices and future reactors. Modelling plasma evolution using numerical solvers is often expensive, consuming many hours on supercomputers, and hence, we need alternative inexpensive surrogate models. We demonstrate accurate predictions of plasma evolution both in simulation and experimental domains using deep learning-based surrogate modelling tools, viz., Fourier Neural Operators (FNO). We show that FNO has a speedup of six orders of magnitude over traditional solvers in predicting the plasma dynamics simulated from magnetohydrodynamic models, while maintaining a high accuracy (MSE ≈ 10−5). Our modified version of the FNO is capable of solving multi-variable Partial Differential Equations (PDE), and can capture the dependence among the different variables in a single model. FNOs can also predict plasma evolution on real-world experimental data observed by the cameras positioned within the MAST Tokamak, i.e., cameras looking across the central solenoid and the divertor in the Tokamak. We show that FNOs are able to accurately forecast the evolution of plasma and have the potential to be deployed for real-time monitoring. We also illustrate their capability in forecasting the plasma shape, the locations of interactions of the plasma with the central solenoid and the divertor for the full duration of the plasma shot within MAST. The FNO offers a viable alternative for surrogate modelling as it is quick to train and infer, and requires fewer data points, while being able to do zero-shot super-resolution and getting high-fidelity solutions.

“…In general, machine learning-based methods have been used in nuclear fusion research in various settings, for example in disruption prediction [21,22], diagnostic processing [23,24] or for accelerating simulation [25,26]; see [27] for an exhaustive overview. Adjacent to our setting, NN-based surrogates have been proposed for accelerating scrape-off layer (SOL) simulation [28,29]. For control purposes, [30] employ sparse regression techniques (SINDy [31]) on SOLPS-ITER simulations to identify reduced models of key boundary plasma quantities.…”

Section: Introductionmentioning

confidence: 99%

Fast dynamic 1D simulation of divertor plasmas with neural PDE surrogates

Poels,

Derks,

Westerhof

et al. 2023

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Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool to understand and control these plasmas, however, for real-time applications or exhaustive parameter scans only simple approximations are currently fast enough. We address this lack of fast simulators using neural PDE surrogates, data-driven neural network-based surrogate models trained using solutions generated with a classical numerical method. The surrogate approximates a time-stepping operator that evolves the full spatial solution of a reference physics-based model over time. We use DIV1D, a 1D dynamic model of the divertor plasma, as reference model to generate data. DIV1D's domain covers a 1D heat flux tube from the X-point (upstream) to the target. We simulate a realistic TCV divertor plasma with dynamics induced by upstream density ramps and provide an exploratory outlook towards fast transients. State-of-the-art neural PDE surrogates are evaluated in a common framework and extended for properties of the DIV1D data. We evaluate (1) the speed-accuracy trade-off; (2) recreating non-linear behavior; (3) data efficiency; and (4) parameter inter- and extrapolation. Once trained, neural PDE surrogates can faithfully approximate DIV1D's divertor plasma dynamics at sub real-time computation speeds: In the proposed configuration, 2 ms of plasma dynamics can be computed in ≈ 0.63ms of wall-clock time, several orders of magnitude faster than DIV1D.