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We investigate the reconstruction of a turbulent flow field in the atmospheric boundary layer from a time series of lidar measurements, using Large-Eddy Simulations (LES) and a 4D-Var data assimilation algorithm. This leads to an…
Disruptions are a serious issue in tokamaks. In a disruption, the thermal energy is lost by means of an instability which could be a resistive wall tearing mode (RWTM). During precursors to a disruption, the plasma edge region cools,…
In magnetically-confined plasmas of tokamaks, neutral deuterium/hydrogen (D/H) atoms play a role in energy, momentum, and particle balance, as well as the stabilization of plasma turbulence. One key important fusion performance parameter is…
We construct a model for the turbulence near the X-point of a Tokamak device and, under suitable assumptions, we arrive to a closed equation for the electric field potential fluctuations. The analytical and numerical analysis is focused on…
We present recent results and technical breakthroughs for the Bayesian inference of tokamak equilibria using force-balance as a prior constraint. Issues surrounding model parameter representation and posterior analysis are discussed and…
We propose a supervised machine learning approach for boosting existing signal and image recovery methods and demonstrate its efficacy on example of image reconstruction in computed tomography. Our technique is based on a local nonlinear…
The contribution of a time-independent toroidally-symmetric radial electric field $E_r$ is implemented in VisualStart [Comp. Phys. Comm. 275 (2022) 108305; arXiv:2111.08224], a code whose purposes include the construction of guiding center…
Machine learning models are exceptionally effective in capturing complex non-linear relationships of high-dimensional datasets and making accurate predictions. However, their intrinsic ``black-box'' nature makes it difficult to interpret…
The Tactical Driver Behavior modeling problem requires understanding of driver actions in complicated urban scenarios from a rich multi modal signals including video, LiDAR and CAN bus data streams. However, the majority of deep learning…
While artificial intelligence (AI) has been promising for fusion control, its inherent black-box nature will make compliant implementation in regulatory environments a challenge. This study implements and validates a real-time AI enabled…
The optimal configuration choice between positive triangularity (PT) and negative triangularity (NT) tokamaks for fusion power plants hinges on navigating different operational constraints rather than achieving specific plasma performance…
Radiated power information is crucial to diagnose and optimize the performance of fusion plasmas. Traditionally, at the TCV tokamak, radiated power analysis has only ever been possible following plasma discharge termination. However,…
Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment…
Methodologies for combining the accuracy of data-driven models with extrapolability of physics-based models are described and tested, for the task of building transport models of tokamak fusion reactors that extrapolate well to new…
New plasma regimes with high confinement, low core impurity accumulation and small Edge localized mode (ELMs) perturbations have been obtained close to ITER conditions in magnetically confined plasmas from the Joint European torus (JET)…
Numerical simulations of plasma flows are crucial for advancing our understanding of microscopic processes that drive the global plasma dynamics in fusion devices, space, and astrophysical systems. Identifying and classifying particle…
Estimating heat flux in the nuclear fusion device EAST is a critically important task. Traditional scientific computing methods typically model this process using the Finite Element Method (FEM). However, FEM relies on grid-based sampling…
A formulation of a multi-input single-output closed-loop subspace method for system identification is employed for the purpose of obtaining control-relevant model of the vacuum-plasma response in the Damavand tokamak. Such a model is…
Layer-wise model fusion via optimal transport, named OTFusion, applies soft neuron association for unifying different pre-trained networks to save computational resources. While enjoying its success, OTFusion requires the input networks to…
The magnetohydrodynamics (MHD) equations are continuum models used in the study of a wide range of plasma physics systems, including the evolution of complex plasma dynamics in tokamak disruptions. However, efficient numerical solution…