Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices

• URL: http://arxiv.org/abs/2609.11628v1
• Date: Thu, 10 Sep 2026 14:40:32 GMT
• Title: Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices
• Abstract summary: We develop an inverse Physics-Informed Neural Network framework to infer the dependence of the scrape-off layer (SOL) perpendicular heat conductivity on plasma density and temperature.<n>The framework is validated using synthetic data generated from a prescribed conductivity function.<n>A scan in the number of plasma profiles used for training and the number of radial measurement positions per profile identifies a practical trade-off between reconstruction accuracy and data availability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we develop an inverse Physics-Informed Neural Network (PINN) framework to infer the dependence of the scrape-off layer (SOL) perpendicular heat conductivity on plasma density and temperature, $κ_\perp(n,T)$. The method combines radial profile measurements of electron density and temperature with the residual of a reduced one-dimensional SOL transport equation, so that the inferred conductivity is constrained by both the measurements and the underlying transport model. Three neural networks are trained simultaneously: two reconstruct the temperature and density profiles as functions of the radial coordinate and transported power, while a third represents the effective conductivity as a function of the local density and temperature. The framework is first validated using synthetic data generated from a prescribed conductivity function, allowing the inferred $κ_\perp(n,T)$ to be compared directly with the ground truth. The model recovers the imposed functional dependence with errors below $10~\%$ in the data-constrained region. Bootstrap resampling is shown to provide a practical indicator of prediction reliability and consistency. A scan in the number of plasma profiles used for training and the number of radial measurement positions per profile identifies a practical trade-off between reconstruction accuracy and data availability. Finally, the method is applied to an experimental dataset from the TJ-II stellarator obtained with the helium-beam diagnostic. This exploratory application provides an initial estimate of the effective SOL conductivity and illustrates the potential of inverse PINNs for extracting transport information from plasma edge measurements.

Related papers

• A hybrid analytical-PINN model for subsurface simulation of geothermal heat exchangers in heterogeneous underground [0.0]
  We propose a physics-informed neural network to approximate a universal corrector with respect to a single borehole with unit heat extraction rate.<n>We perform numerical tests to exhibit the effectiveness of the proposed method based on three different analytical models.
  arXiv  Detail & Related papers  (2026-07-14T02:17:30Z)
• Physics-Informed Machine Learning for Pouch Cell Temperature Estimation [5.435523531334183]
  This paper presents a physics-informed machine learning (PIML) framework for the efficient estimation of steady-state temperature profiles.<n>The PIML approach integrates the governing heat transfer equations directly into the neural network's loss function, enabling high-fidelity predictions.<n>Results demonstrate that the PIML model converges more rapidly and achieves markedly higher accuracy, with a 49.1% reduction in mean squared error over the data-driven model.
  arXiv  Detail & Related papers  (2026-04-16T02:58:10Z)
• Physics-Informed Neural Network Digital Twin for Dynamic Tray-Wise Modeling of Distillation Columns under Transient Operating Conditions [0.0]
  Digital twin technology offers transformative capabilities for industrial process monitoring, control, and optimization.<n>This work presents a Physics-Informed Neural Network (PINN) digital twin framework for the dynamic, tray-wise modeling of binary distillation columns operating under transient conditions.<n>Results establish the proposed PINN digital twin as a robust foundation for real-time soft sensing, model-predictive control, and anomaly detection in industrial distillation processes.
  arXiv  Detail & Related papers  (2026-03-25T14:45:35Z)
• Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage [65.51149575007149]
  We present Fun-DDPS, a generative framework that combines function-space diffusion models with differentiable neural operator surrogates for both forward and inverse modeling.<n>Fun-DDPS produces physically consistent realizations free from the high-frequency artifacts observed in joint-state baselines.
  arXiv  Detail & Related papers  (2026-02-12T18:58:12Z)
• Decoupled Diffusion Sampling for Inverse Problems on Function Spaces [73.52103661482242]
  Existing plug-and-play diffusion posterior samplers represent physics implicitly through coefficient joint-solution modeling.<n>We propose a physics-aware generative framework in function space for inverse PDE problems.<n>Our Decoupled Diffusion Inverse Solver (DDIS) employs a decoupled design: an unconditional diffusion learns the coefficient prior, while a neural operator explicitly models the forward PDE for guidance.
  arXiv  Detail & Related papers  (2026-01-30T18:54:49Z)
• Physics Informed Neural Networks for design optimisation of diamond particle detectors for charged particle fast-tracking at high luminosity hadron colliders [70.66815108184498]
  Future high-luminosity hadron colliders demand tracking detectors with extreme radiation tolerance, high spatial precision, and sub-nanosecond timing.<n>3D diamond pixel sensors offer these capabilities due to diamond's radiation hardness and high carrier mobility.<n>We model the phenomenon through a 3rd-order, 3+1D PDE derived as a quasi-stationary approximation of Maxwell's equations.
  arXiv  Detail & Related papers  (2025-09-25T13:09:28Z)
• Revisiting Heat Flux Analysis of Tungsten Monoblock Divertor on EAST using Physics-Informed Neural Network [13.813339681786644]
  Estimating heat flux in the nuclear fusion device EAST is a critically important task.<n>FEM relies on grid-based sampling for computation, which is computationally inefficient and hard to perform real-time simulations.<n>This paper proposes a novel Physics-Informed Neural Network (PINN) to address this challenge.
  arXiv  Detail & Related papers  (2025-08-05T12:42:02Z)
• Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
  Entropy and mutual information in neural networks provide rich information on the learning process. We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
  arXiv  Detail & Related papers  (2023-12-04T01:32:42Z)
• Deep Learning assisted microwave-plasma interaction based technique for plasma density estimation [1.4680035572775534]
  The article proposes a Deep Learning (DL) assisted microwave-plasma interaction-based non-invasive diagnostics. The electric field pattern due to microwave scattering from plasma is utilized to estimate density profile. The obtained results show promising performance in estimating the 2D radial profile of the density for the given linear plasma device.
  arXiv  Detail & Related papers  (2023-04-28T12:27:23Z)
• Simultaneous boundary shape estimation and velocity field de-noising in Magnetic Resonance Velocimetry using Physics-informed Neural Networks [70.7321040534471]
  Magnetic resonance velocimetry (MRV) is a non-invasive technique widely used in medicine and engineering to measure the velocity field of a fluid. Previous studies have required the shape of the boundary (for example, a blood vessel) to be known a priori. We present a physics-informed neural network that instead uses the noisy MRV data alone to infer the most likely boundary shape and de-noised velocity field.
  arXiv  Detail & Related papers  (2021-07-16T12:56:09Z)
• Adiabatic Sensing Technique for Optimal Temperature Estimation using Trapped Ions [64.31011847952006]
  We propose an adiabatic method for optimal phonon temperature estimation using trapped ions. The relevant information of the phonon thermal distributions can be transferred to the collective spin-degree of freedom. We show that each of the thermal state probabilities is adiabatically mapped onto the respective collective spin-excitation configuration.
  arXiv  Detail & Related papers  (2020-12-16T12:58:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.