Current Research into Applications of Tomography for Fusion Diagnostics

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Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbacks compared to traditional feedback control approaches for magnetic confinem ...

Plasmas with negative triangularity (NT) shape have been recently shown to be able to achieve H-mode levels of confinement in L-mode, avoiding detrimental edge localised modes. Therefore, this plasma geometry is now studied as a possible viable option for ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

The pre-thermal quench (pre-TQ) dynamics of a pure deuterium ( D 2 ) shattered pellet injection (SPI) into a 3 MA / 7 MJ JET H-mode plasma is studied via 3D non-linear MHD modelling with the JOREK code. The interpretative modelling captures the overall evo ...

Carbon impurity transport is studied in the TCV tokamak using a charge exchange recombination diagnostic. TCVs flexible shaping capabilities were exploited to extend previous impurity transport studies to negative triangularity (delta < 0). A practical way ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Rapid inter-discharge simulation and optimization using the RAPTOR code have allowed the development of a reliable and reproducible early heating strategy for an advanced tokamak (AT) scenario on ASDEX Upgrade. Solving for electron heat and current diffusi ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Current Research into Applications of Tomography for Fusion Diagnostics

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Towards practical reinforcement learning for tokamak magnetic control

First-principle based predictions of the effects of negative triangularity on DTT scenarios

Gibbs sampling the posterior of neural networks

Interpretative 3D MHD modelling of deuterium SPI into a JET H-mode plasma

Study of impurity C transport and plasma rotation in negative triangularity on the TCV tokamak

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Machine Learning for Modeling Stock Returns

Inter-discharge optimization for fast, reliable access to ASDEX Upgrade advanced tokamak scenario

Generalization of Scaled Deep ResNets in the Mean-Field Regime