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JET experiments using the fuel mixture envisaged for fusion power plants, deuterium and tritium (D–T), provide a unique opportunity to validate existing D–T fusion power prediction capabilities in support of future device design and operation preparation. The 2021 JET D–T experimental campaign has achieved D–T fusion powers sustained over 5 s in ITER-relevant conditions i.e. operation with the baseline or hybrid scenario in the full metallic wall. In preparation of the 2021 JET D–T experimental campaign, extensive D–T predictive modelling was carried out with several assumptions based on D discharges. To improve the validity of ITER D–T predictive modelling in the future, it is important to use the input data measured from 2021 JET D–T discharges in the present core predictive modelling, and to specify the accuracy of the D–T fusion power prediction in comparison with the experiments. This paper reports on the validation of the core integrated modelling with TRANSP, JINTRAC, and ETS coupled with a quasilinear turbulent transport model (Trapped Gyro Landau Fluid or QualLiKiz) against the measured data in 2021 JET D–T discharges. Detailed simulation settings and the heating and transport models used are described. The D–T fusion power calculated with the interpretive TRANSP runs for 38 D–T discharges (12 baseline and 26 hybrid discharges) reproduced the measured values within 20 % . This indicates the additional uncertainties, that could result from the measurement error bars in kinetic profiles, impurity contents and neutron rates, and also from the beam-thermal fusion reaction modelling, are less than 20 % in total. The good statistical agreement confirms that we have the capability to accurately calculate the D–T fusion power if correct kinetic profiles are predicted, and indicates that any larger deviation of the D–T fusion power prediction from the measured fusion power could be attributed to the deviation of the predicted kinetic profiles from the measured kinetic profiles in these plasma scenarios. Without any posterior adjustment of the simulation settings, the ratio of predicted D–T fusion power to the measured fusion power was found as 65%–96% for the D–T baseline and 81%–97% for D–T hybrid discharge. Possible reasons for the lower D–T prediction are discussed and future works to improve the fusion power prediction capability are suggested. The D–T predictive modelling results have also been compared to the predictive modelling of the counterpart D discharges, where the key engineering parameters are similar. Features in the predicted kinetic profiles of D–T discharges such as underprediction of n e are also found in the prediction results of the counterpart D discharges, and it leads to similar levels of the normalized neutron rate prediction between the modelling results of D–T and the counterpart D discharges. This implies that the credibility of D–T fusion power prediction could be a priori estimated by the prediction quality of the preparatory D discharges, which will be attempted before actual D–T experiments.

The need for microsecond speed machine learning (ML) inference for particle physics experiments has emerged in recent years, in particular for the forthcoming upgrades to the experiments at the Large Hadron Collider at CERN. A community has grown around the need to develop the custom hardware platforms and tools required. The material presented in this report is drawn from the latest workshop held by the fast ML for science community and comprises of a collection of perspectives on the status of fast ML in different scientific domains, and the supporting technology.

The theory, development, and validation of reduced quasilinear models of gyrokinetic turbulent transport in the closed flux surface core of tokamaks is reviewed. In combination with neoclassical collisional transport, these models are successful in accurately predicting core tokamak plasma temperature, density, rotation, and impurity profiles in a variety of confinement regimes. Refined experimental tests have been performed to validate the predictions of the quasilinear models, probing changes in the dominant gyrokinetic instabilities, as reflected in fluctuation measurements, cross-phases, and transport properties. These tests continue to produce a deeper understanding of the complex mix of instabilities at both electron and ion gyroradius scales.

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis. This Perspective discusses how high-energy-density physics could tap the potential of AI-inspired algorithms for extracting relevant information and how data-driven automatic control routines may be used for optimizing high-repetition-rate experiments.

Large scale validation and uncertainty quantification are essential in the experimental design, control, and operations of fusion reactors. Reduced models and increasing computational power means that it is possible to run many simulations, yet setting up simulation runs remaining a time-consuming and error-prone process that involves many manual steps. duqtools is an open-source workflow tool written in Python for that addresses this bottleneck by automating the set up of new simulations. This enables uncertainty quantification and large scale validation of fusion energy modelling simulations. In this work, we demonstrate how duqtools can be used to set up and launch 2000 different simulations of plasma experiments to validate aspects of the JINTRAC modelling suite. With this large-scale validation we identified issues in preserving data consistency in model initialization of the current (\\(I(p)\\)) distribution. Furthermore, we used duqtools for sensitivity analysis on the QLKNN-jetexp-15D surrogate model to verify its correctness in multiple regimes.

Large scale validation and uncertainty quantification are essential in the experimental design, control, and operations of fusion reactors. Reduced models and increasing computational power means that it is possible to run many simulations, yet setting up simulation runs remaining a time-consuming and error-prone process that involves many manual steps. duqtools is an open-source workflow tool written in Python for that addresses this bottleneck by automating the set up of new simulations. This enables uncertainty quantification and large scale validation of fusion energy modelling simulations. In this work, we demonstrate how duqtools can be used to set up and launch 2000 different simulations of plasma experiments to validate aspects of the JINTRAC modelling suite. With this large-scale validation we identified issues in preserving data consistency in model initialization of the current (\\(I(p)\\)) distribution. Furthermore, we used duqtools for sensitivity analysis on the QLKNN-jetexp-15D surrogate model to verify its correctness in multiple regimes.

A novel combination of two widely-used clustering algorithms is proposed here for the detection and reduction of high data density regions. The Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used for the detection of high data density regions and the k-means algorithm for reduction. The proposed algorithm iterates while successively decrementing the DBSCAN search radius, allowing for an adaptive reduction factor based on the effective data density. The algorithm is demonstrated for a physics simulation application, where a surrogate model for fusion reactor plasma turbulence is generated with neural networks. A training dataset for the surrogate model is created with a quasilinear gyrokinetics code for turbulent transport calculations in fusion plasmas. The training set consists of model inputs derived from a repository of experimental measurements, meaning there is a potential risk of over-representing specific regions of this input parameter space. By applying the proposed reduction algorithm to this dataset, this study demonstrates that the training dataset can be reduced by a factor ~20 using the proposed algorithm, without a noticeable loss in the surrogate model accuracy. This reduction provides a novel way of analyzing existing high-dimensional datasets for biases and consequently reducing them, which lowers the cost of re-populating that parameter space with higher quality data.

Within integrated tokamak plasma modelling, turbulent transport codes are typically the computational bottleneck limiting their routine use outside of post-discharge analysis. Neural network (NN) surrogates have been used to accelerate these calculations while retaining the desired accuracy of the physics-based models. This paper extends a previous NN model, known as QLKNN-hyper-10D, by incorporating the impact of impurities, plasma rotation and magnetic equilibrium effects. This is achieved by adding a light impurity fractional density (\\(n_imp,light / n_e\\)) and its normalized gradient, the normalized pressure gradient (\\(\\)), the toroidal Mach number (\\(M_tor\\)) and the normalized toroidal flow velocity gradient. The input space was sampled based on experimental data from the JET tokamak to avoid the curse of dimensionality. The resulting networks, named QLKNN-jetexp-15D, show good agreement with the original QuaLiKiz model, both by comparing individual transport quantity predictions as well as comparing its impact within the integrated model, JINTRAC. The profile-averaged RMS of the integrated modelling simulations is <10% for each of the 5 scenarios tested. This is non-trivial given the potential numerical instabilities present within the highly nonlinear system of equations governing plasma transport, especially considering the novel addition of momentum flux predictions to the model proposed here. An evaluation of all 25 NN output quantities at one radial location takes \\(\\)0.1 ms, \\(10^4\\) times faster than the original QuaLiKiz model. Within the JINTRAC integrated modelling tests performed in this study, using QLKNN-jetexp-15D resulted in a speed increase of only 60 - 100 as other physics modules outside of turbulent transport become the bottleneck.

This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques were applied for profile fitting during the preparation of integrated modelling simulations allowing for rigourous sensitivity tests of prescribed initial and boundary conditions as both fit and derivative uncertainties are provided. This was demonstrated by a JETTO integrated modelling simulation of the JET ITER-like-wall H-mode baseline discharge #92436 with the QuaLiKiz quasilinear turbulent transport model, which is the subject of extrapolation towards a deuterium-tritium plasma. The simulation simultaneously evaluates the time evolution of heat, particle, and momentum fluxes over \\(10\\) confinement times, with a simulation boundary condition at \\(_tor = 0.85\\). Routine inclusion of momentum transport prediction in multi-channel flux-driven transport modelling is not standard and is facilitated here by recent developments within the QuaLiKiz model. Excellent agreement was achieved between the fitted and simulated profiles for \\(n_e\\), \\(T_e\\), \\(T_i\\), and \\(_tor\\) within \\(2\\), but the simulation underpredicts the mid-radius \\(T_i\\) and overpredicts the core \\(n_e\\) and \\(T_e\\) profiles for this discharge. Despite this, it was shown that this approach is capable of deriving reasonable inputs, including derivative quantities, to tokamak models from experimental data. Furthermore, multiple figures-of-merit were defined to quantitatively assess the agreement of integrated modelling predictions to experimental data within the GPR profile fitting framework.

In order to predict and analyze turbulent transport in tokamaks, it is important to model transport that arises from microinstabilities. For this task, quasilinear codes have been developed that seek to calculate particle, angular momentum, and heat fluxes both quickly and accurately. In this tutorial, we present a derivation of one such code known as QuaLiKiz, a quasilinear gyrokinetic transport code. The goal of this derivation is to provide a self-contained and complete description of the underlying physics and mathematics of QuaLiKiz from first principles. This work serves both as a comprehensive overview of QuaLiKiz specifically as well as an illustration for deriving quasilinear models in general.