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Egemen Kolemen

Publications and source records attributed to Egemen Kolemen.

At least 19 recordsLinked to original sources
arXiv2026

Extension of a multi-region free-surface MHD solver beyond the inductionless approximation

Free-surface liquid metal flows are a leading candidate for the plasma-facing components of future fusion reactors, but existing transient, three-dimensional, free-surface MHD solvers rely on the inductionless approximation, in which the induced magnetic field is neglected. This paper extends the open-source solver FreeMHD [B. Wynne et al., Phys. Plasmas 32, 013907 (2025)] beyond that approximation, resolving the induced field self-consistently with a vector-potential formulation that enforces $\nabla\cdot\boldsymbol{B}=0$ by construction while preserving the original multi-region, two-phase framework. It is verified against the analytical Shercliff and Hunt duct flows, against flows driven by a time-varying applied field, and against the deformation of a free liquid metal jet crossing a non-uniform field, and validated against free-surface height measurements from the LMX-U experiment. The experiment validates the overall free-surface solution rather than finite-$R_m$ effects, which the transient-field cases verify. To our knowledge this is the first open-source, fully three-dimensional free-surface liquid metal solver to resolve the evolution of the induced magnetic field, providing a basis for modeling the finite magnetic Reynolds number conditions expected in large-scale, transient fusion events.

physics.comp-ph↗
arXiv2026

Is Your AI Fast Enough to Run a Fusion Reactor?

Machine learning models are increasingly used in feedback control loops for nuclear fusion, where inference speed and predictable timing are critical. We summarize lessons from models deployed for control on the DIII-D tokamak and develop a benchmark to compare inference backends across ten neural networks and model components from fusion control and diagnostic pipelines. For models greater than five million parameters, the CPU backends take tens to thousands of milliseconds, while GPU inference is substantially faster, suggesting an upper limit on CPU-oriented development for control. These results show why the deployment backend must be selected together with the model and its control-cycle budget.

physics.plasm-ph↗
arXiv2026

IGNITE Tokamak World Model Architecture

We introduce IGNITE, a generative world foundation model for fusion plasma behavior simulation trained in a self-supervised manner from over a decade of unlabeled experimental data at the DIII-D National Fusion Facility. The core of IGNITE is a dynamics model that can simulate DIII-D discharges from a given set of actuator trajectories. These trajectories can be supplied or generated on-the-fly from a textual prompt or from desired experimental outcomes. The model architecture consists of several spatio-temporal tokenizers that embed the different input modalities, including time-series like spatio-temporal measurement data, image sequences, and high-resolution spectrograms, each of which collected at vastly different time scales. The backbone is composed of an auto-regressive dynamics model that has the capacity to predict entire DIII-D discharges given initial latent plasma states and actuator trajectories over a theoretical infinite horizon. IGNITE paves the way towards efficient AI-driven experimental planning and world modeling for nuclear fusion.

physics.plasm-ph↗
arXiv2026

Applications of Poincaré Boundary Condition for 3D Ideal MHD Equilibrium and Optimization

Stellarator ideal magnetohydrodynamic (MHD) codes that assume nested flux surfaces such as \texttt{VMEC} and \texttt{DESC} solve the equilibrium problem by prescribing the total toroidal magnetic flux, plasma profiles and the last closed flux surface (LCFS), which analytically determines the unique field in vacuum, whereas at finite $β$ bifurcations and distinct equilibria sharing the same boundary have been reported in the literature. In this paper, we propose prescribing the Poincaré cross-section of the field at a single toroidal plane, and implement it in \texttt{DESC}, where the new condition enters only through the linear constraints and therefore costs no more than a fixed-LCFS solve. Fixing the geometry on one plane rather than on a full toroidal surface generally leaves more of the spectral coefficients free, and the ones it frees are those carrying the toroidal variation of the boundary flux surface, which a prescribed LCFS holds fixed at every toroidal angle; together these allow better-converged numerical solutions. We solve equilibria with the cross-section held fixed, starting either from the axisymmetric shape obtained by revolving that cross-section toroidally, or from an existing fixed-LCFS solution. In the latter case, the volume-averaged normalized force error falls by an order of magnitude while the configuration stays close to the original one, and re-solving the resulting boundary with the conventional fixed-LCFS solver recovers the same equilibrium. We further show that the Poincaré coefficients can be used directly as design variables by optimizing a quasi-helical configuration that maintains high-fidelity force balance throughout the process.

physics.plasm-ph↗
arXiv2026

Towards Large-Scale Heterogeneous Data Organization for Scientific Foundation Models: A Nuclear Fusion Case Study

Training effective foundation models requires massive and organized datasets, yet scientific domains such as nuclear fusion present unique challenges due to largely heterogeneous and sparse data. Here we characterize the data used in developing such a model: with over 20 sensor types spanning 5 orders of magnitude in sampling rate, mixed tensor structures (point measurements, spectrograms, images), and nonstationary physics. We analyze our input complexity and discuss trade-offs between temporal context and frequency resolution. Our analysis provides a template for representing multi-modal fluctuation data at scale, with implications for both multi-modal control systems and nuclear fusion.

physics.plasm-ph↗
arXiv2026

Characterization of ELM Pacing via Vertical Jogs on DIII-D

Edge localized mode (ELM) pacing via vertical plasma oscillations or jogging has been successfully demonstrated on DIII-D. Rapid vertical movement of the plasma toward the X-point has been shown to effectively trigger ELMs. By vertically oscillating the plasma at a rate of 10 Hz, the ELM frequency increased from $\sim$5~Hz, the natural ELM frequency in similar DIII-D discharges, to 10~Hz. Downward jogs have been observed to trigger multiple ELMs in one cycle. ELMs triggered at higher than natural frequencies lead to smaller decreases in stored energy, from ~10\% to as little as below 1\%. As a consequence, the peak heat flux to the divertor has been observed to be reduced by a factor of $\sim$2. In addition, a reduction in the carbon impurity concentration has been observed. During downward jogs in the lower single null (LSN) configuration, the X-point movement is slower and smaller than the top of the plasma. As a result, a reduction in the plasma cross section and hence volume has been observed. To understand the mechanism of ELM triggering by jogging, a toy model of the edge toroidal current has been built and tested with DIII-D experiment data. The experimental data and model suggest that when the plasma moves down towards the X-point, a net positive toroidal current is locally induced in the edge region. ELITE stability analysis suggests that this current pushes the plasma state across the peeling side of the peeling-ballooning stability boundary into the unstable region triggering ELMs.

physics.plasm-ph↗
arXiv2026

Real-time feedback control of ELM frequency using divertor gas puffing and its effects on tungsten-induced radiation and plasma performance in KSTAR

The edge-localized mode (ELM) frequency ($f_{\mathrm{ELM}}$) was successfully controlled in real time on KSTAR using a proportional-integral (PI) feedback controller, employing a $\mathrm{D}_2$ divertor gas puff as the actuator under tungsten lower-divertor conditions. The controller accurately tracked a two-step target---a 30 Hz increase in $f_{\mathrm{ELM}}$ for 4 s, followed by a 30 Hz decrease for 3 s---yielding mean and median absolute percentage errors of approximately 13% and 12%, respectively. Compared to a reference discharge, the actively controlled shot did not exhibit a significant drop in volume-integrated core radiation, confirming that excessive gas use merely degrades overall plasma performance. However, when contrasted with the exponential increase in core radiation observed in the absence of divertor gas puffing, these results underscore the critical need for real-time optimization. Specifically, divertor gas commands must be actively managed to maintain an $f_{\mathrm{ELM}}$ sufficient for flushing tungsten from the core while maximizing global plasma performance.

physics.plasm-ph↗
arXiv2026

One-shot acceleration of transient PDE solvers via online-learned preconditioners

Data-driven acceleration of scientific computing workflows has been a high-profile aim of machine learning (ML) for science, with numerical simulation of transient partial differential equations (PDEs) being one of the main applications. The focus thus far has been on methods that require classical simulations to train, which when combined with the data-hungriness and optimization challenges of neural networks has caused difficulties in demonstrating a convincing advantage against strong classical baselines. We consider an alternative paradigm in which the learner uses a classical solver's own data to accelerate it, enabling a one-shot speedup of the simulation. Concretely, since transient PDEs often require solving a sequence of related linear systems, the feedback from repeated calls to a linear solver such as preconditioned conjugate gradient (PCG) can be used by a bandit algorithm to online-learn an adaptive sequence of solver configurations (e.g. preconditioners). The method we develop, PCGBandit, is implemented directly on top of the popular open-source software OpenFOAM, which we use to show its effectiveness on a set of fluid and magnetohydrodynamics (MHD) problems.

physics.comp-ph↗
arXiv2026

Spectrally accurate, reverse-mode differentiable bounce-averaging algorithm and its applications

We present a fast, spectrally (exponentially) accurate, automatically differentiable bounce-averaging algorithm that is used to simplify kinetic models. Using this algorithm, implemented in the DESC stellarator optimisation suite, we can perform efficient optimisation of many objectives to improve stellarator performance, such as the effective ripple $ε_{\mathrm{eff}}$ metric for the neoclassical transport coefficient in the low collisionality regime, energetic particle confinement, and turbulent transport. For the first time, we optimise a finite-beta stellarator to directly reduce neoclassical ripple transport using reverse-mode differentiation. This ensures the computational cost of differentiation is independent of the number of controllable parameters.

physics.plasm-ph↗
arXiv2026

Offline Reinforcement Learning for Rotation Profile Control in Tokamaks

Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficult or unsolved. One such challenge is controlling the plasma rotation profile, which strongly influences stability, confinement, and transport. While the average rotation can be controlled, controlling the full profile is challenging due to high dimensionality, response to multiple actuators and dependence on plasma condition. Learning-based control methods, such as reinforcement learning (RL), provide a potential solution to this challenging problem with ability to model complex interactions leading to effective multi-input multi-output control. However, learning such policies is challenging due to the lack of accurate simulators that can model the rotation profile dynamics. In this work, we investigate the use of offline RL and offline model-based RL algorithms for rotation profile control, training them solely on historical data from the DIII-D tokamak. Our final method uses probabilistic models of plasma dynamics to generate rollouts for RL training. We deploy this policy on the DIII-D Tokamak and observe promising real-world results. We conclude by highlighting key challenges and insights from training and deploying an RL policy on a complex physical device while using only limited past data.

cs.LG↗
arXiv2026

Lithium Experimental Application Platform (LEAP): Secondary-Containment Architecture for Flowing Liquid Lithium in Fusion Systems

Flowing liquid lithium is a promising fusion technology because it can provide a renewable Plasma-Facing Component (PFC) surface, modify recycling, support power exhaust, and potentially connect plasma-facing components with fuel recovery. Its deployment, however, is limited by the need to manage chemical reactivity, fire and aerosol hazards, inert gas operation, maintainability, and rapid experimental iteration. This paper develops a semi-quantitative hazard complexity framework for selecting secondary containment architectures for flowing liquid lithium systems. The framework is applied to six representative containment scenarios and to the Lithium Experimental Application Platform (LEAP) at Princeton Plasma Physics Laboratory. LEAP is under construction with a modular, room-scale argon gloveroom as an inert secondary containment boundary for a staged flowing lithium program with heating, diagnostics, magnetic field exposure, and future device interface capability. The analysis shows that an inert, airtight secondary enclosure without scrubbers around a liquid lithium loop provides a practical balance between hazard reduction and facility complexity, as defined by the design requirements. The resulting architecture offers a deployable path for lithium PFC development and a transferable design logic for other reactive or conductive liquid metal systems.

physics.app-ph↗
arXiv2026

FPGA-Accelerated Real-Time Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference

In this work, we demonstrate the deployment of a hardware-accelerated machine learning (ML) inference system integrated into a real-time processing at the DIII-D tokamak fusion reactor. The team has successfully deployed an AMD/Xilinx KCU1500 field-programmable gate array (FPGA) into the realtime Plasma Control System (PCS) nodes that receives the live Beam Emission Spectroscopy (BES) signal used for Edge Localized Mode (ELM) forecasting. The FPGA hosts a dense neural network using the SLAC Neural Network Library (SNL) that has been trained to infer the likelihood of disruptive ELM conditions. This likelihood then feeds a separate plasma controller that uses Resonant Magnetic Perturbation coils to suppress the predicted disruptive condition. The SNL allows for on-the-fly updates of the neural network weights and biases without requiring full hardware resynthesis for the FPGA. Judicious design of the neural-network architecture can further allow for the hot-swapping of multiple classification tasks to be executed on the single FPGA, significantly enhancing the real-time adaptability of the system for context-aware control strategies that respond in real-time to evolving reactor conditions. These adaptive weights naturally support continuous model refinement and seamless task switching during live experimental operation. This use case is chosen as a high rate signal processing example that can serve as a template for general ML-based reactor diagnostic processing for active reactor control systems. We see this as an essential development for achieving reactor relevant operation in future continuous operation fusion devices.

physics.plasm-ph↗
arXiv2026

Revisiting confinement scalings and fusion performance with a perspective optimized for extrapolation

Recent advances in high-temperature-superconductor technology have made substantially higher toroidal magnetic fields technologically accessible, reopening the design space for compact, high-field tokamak reactors. Because reactor performance projections remain anchored to empirical confinement scalings, the recent update to the ITPA global H-mode confinement database raises an important question: what does the present experimental record and its uncertainty imply for the path to reactor-grade fusion performance? In this work, we revisit confinement extrapolation from an explicitly extrapolation-oriented perspective and, to complement its implications in terms of a direct reactor performance measure, present a cross-machine empirical scaling for fusion power. We systematically search for a minimally complex confinement scaling that optimizes the tradeoff between variance capture and extrapolative robustness. We find that low-order models centered near $N=3$ to $N=4$ optimize this tradeoff, with plasma current, machine size, heating power, and elongation emerging as the dominant engineering levers, together with an empirically inferred confinement penalty associated with metallic walls. Recast in reactor-performance terms, the results indicate that both the fusion triple product and fusion power are governed primarily by plasma current: the triple product scales approximately as $I_p^2$, and the empirical fusion power scaling exhibits a similarly near-quadratic dependence over a survey of the highest performing discharges across several machines. Projecting to reactors, these results suggest that high-field devices with metal walls may require higher plasma current than standard IPB98$(y,2)$-based expectations imply, and that gigawatt-class tokamak performance likely demands operation at $I_p \gtrsim 20\mathrm{MA}$.

physics.plasm-ph↗
arXiv2026

Optimizing stellarators with hidden symmetry

Stellarators confine fusion plasmas using three-dimensional magnetic fields composed of nested toroidal magnetic surfaces. In generic stellarators, trapped particles can drift across these surfaces and degrade plasma confinement. Certain topological properties of the magnetic field strength can suppress these drifts. However, conventional stellarator design approaches typically enforce restrictive constraints to realize such properties, thereby segmenting and limiting the accessible configuration space. In this work, we reformulate the conditions for efficient confinement as constraints on a homeomorphic straightening transformation of the field contours. Within this framework, the various families of stellarator magnetic fields optimized for plasma confinement arise naturally as specific realizations of a unified mapping. This new perspective provides a significantly more comprehensive description of viable stellarator configurations, enabling systematic exploration of trade-offs among confinement quality, geometric complexity, and engineering requirements. We illustrate this approach by presenting a highly compact stellarator design that nevertheless achieves plasma performance comparable to that of leading reactor-scale designs with much larger aspect ratios.

physics.plasm-ph↗
arXiv2026

Deflation Techniques for Stellarator Equilibrium and Optimization

Stellarator optimization is a multi-objective, non-convex problem characterized by a complex objective landscape containing many local minima. The solution resulting from a single optimization is highly sensitive to factors such as the initial guess, objective weights, and the optimization method employed. However, merely varying these factors does not guarantee that a physically distinct minimum will be found; optimizations often fail to converge to good minima or simply return to the same or very similar local minima despite large-scale parameter scans. This paper presents a novel application of deflation methods to effectively explore this landscape. By modifying the objective function to penalize and "deflate" away already-found solutions, this technique encourages the optimizer towards attractive, distinct new minima while using a single initial guess and optimization setup. We provide a primer on deflation for nonlinear systems and non-convex optimization before applying it to non-axisymmetric equilibrium and stellarator optimization problems. Key results include the discovery of families of global equilibria with similar core characteristics and the convergence to helical core equilibria without prescient initial guesses. Furthermore, we demonstrate that augmenting stage-one stellarator and stage-two coil optimization with deflation constraints readily produces multiple high-quality, distinct solutions, establishing the method's efficacy and ease of use.

physics.plasm-ph↗
arXiv2026

TokEye: Fast Signal Extraction for Fluctuating Time Series via Offline Self-Supervised Learning From Fusion Diagnostics to Bioacoustics

Next-generation fusion facilities like ITER face a "data deluge," generating petabytes of multi-diagnostic signals daily that challenge manual analysis. We present a "signals-first" self-supervised framework for the automated extraction of coherent and transient modes from high-noise time-frequency data across a variety of sensors. We also develop a general-purpose method and tool for extracting coherent, quasi-coherent, and transient modes for fluctuation measurements in tokamaks by employing non-linear optimal techniques in multichannel signal processing with a fast neural network surrogate on fast magnetics, electron cyclotron emission, CO2 interferometers, and beam emission spectroscopy measurements from DIII-D. Results are tested on data from DIII-D, TJ-II, and non-fusion spectrograms. With an inference latency of 0.5 seconds, this framework enables real-time mode identification and large-scale automated database generation for advanced plasma control. Repository is in https://github.com/PlasmaControl/TokEye.

eess.SP↗
arXiv2026

Beyond the Loss Curve: Scaling Laws, Active Learning, and the Limits of Learning from Exact Posteriors

How close are neural networks to the best they could possibly do? Standard benchmarks cannot answer this because they lack access to the true posterior p(y|x). We use class-conditional normalizing flows as oracles that make exact posteriors tractable on realistic images (AFHQ, ImageNet). This enables five lines of investigation. Scaling laws: Prediction error decomposes into irreducible aleatoric uncertainty and reducible epistemic error; the epistemic component follows a power law in dataset size, continuing to shrink even when total loss plateaus. Limits of learning: The aleatoric floor is exactly measurable, and architectures differ markedly in how they approach it: ResNets exhibit clean power-law scaling while Vision Transformers stall in low-data regimes. Soft labels: Oracle posteriors contain learnable structure beyond class labels: training with exact posteriors outperforms hard labels and yields near-perfect calibration. Distribution shift: The oracle computes exact KL divergence of controlled perturbations, revealing that shift type matters more than shift magnitude: class imbalance barely affects accuracy at divergence values where input noise causes catastrophic degradation. Active learning: Exact epistemic uncertainty distinguishes genuinely informative samples from inherently ambiguous ones, improving sample efficiency. Our framework reveals that standard metrics hide ongoing learning, mask architectural differences, and cannot diagnose the nature of distribution shift.

cs.LG↗
arXiv2025

FPGA-Accelerated Real-Time Beam Emission Spectroscopy Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference

Achieving reliable real-time control of tokamak plasmas is essential for sustaining high-performance operation in next-generation fusion reactors. A major challenge is the accurate and timely prediction of edge-localized modes (ELMs), especially in high-confinement regimes such as wide-pedestal quiescent H-mode. We present a hardware-accelerated machine learning (ML) inference system integrated into the RTSTAB processing node of the DIII-D real-time diagnostic and control infrastructure. The system uses an AMD/Xilinx KCU1500 FPGA to enable ultra low latency plasma state classification and ELM forecasting. Input features come from real-time Beam Emission Spectroscopy (BES), and the ML model is implemented as a dense neural network using the SLAC Neural Network Library (SNL). A key capability is SNL dynamic parameter loading, which allows on-the-fly updates of neural network weights and biases without hardware resynthesis. This enables multiple classification tasks on a single FPGA design and supports adaptive control strategies that respond to evolving plasma conditions. By decoupling inference from fixed-weight configurations, the system supports continuous model refinement and seamless task switching during live operation. The SNL-based inference engine is fully integrated with the FPGA in the DIII-D RTSTAB Plasma Control System (PCS), improving ELM avoidance, confinement, and operational stability. These results show the feasibility of embedding dynamically reconfigurable FPGA-based ML inference into real-time fusion diagnostic pipelines, providing a scalable and resilient path toward intelligent and autonomous plasma control in future magnetic confinement fusion devices.

physics.plasm-ph↗