TGLF-SINN: Deep Learning Surrogate Model for Accelerating Turbulent Transport Modeling in Fusion

• URL: http://arxiv.org/abs/2509.07024v1
• Date: Sun, 07 Sep 2025 09:36:51 GMT
• Title: TGLF-SINN: Deep Learning Surrogate Model for Accelerating Turbulent Transport Modeling in Fusion
• Authors: Yadi Cao, Futian Zhang, Wesley Liu, Tom Neiser, Orso Meneghini, Lawson Fuller, Sterling Smith, Raffi Nazikian, Brian Sammuli, Rose Yu,
• Abstract summary: We propose textbfTGLF-SINN (Spectra-Informed Neural Network) with three key innovations.<n>Our approach achieves superior performance with significantly less training data.<n>In downstream flux matching applications, our NN surrogate provides 45x speedup over TGLF while maintaining comparable accuracy.
• Score: 18.028061388104963
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulations requiring thousands of evaluations remain computationally expensive. Neural network (NN) surrogates offer accelerated inference with fully differentiable approximations that enable gradient-based coupling but typically require large training datasets to capture transport flux variations across plasma conditions, creating significant training burden and limiting applicability to expensive gyrokinetic simulations. We propose \textbf{TGLF-SINN (Spectra-Informed Neural Network)} with three key innovations: (1) principled feature engineering that reduces target prediction range, simplifying the learning task; (2) physics-guided regularization of transport spectra to improve generalization under sparse data; and (3) Bayesian Active Learning (BAL) to strategically select training samples based on model uncertainty, reducing data requirements while maintaining accuracy. Our approach achieves superior performance with significantly less training data. In offline settings, TGLF-SINN reduces logarithmic root mean squared error (LRMSE) by 12. 4\% compared to the current baseline \base. Using only 25\% of the complete dataset with BAL, we achieve LRMSE only 0.0165 higher than \base~and 0.0248 higher than our offline model (0.0583). In downstream flux matching applications, our NN surrogate provides 45x speedup over TGLF while maintaining comparable accuracy, demonstrating potential for training efficient surrogates for higher-fidelity models where data acquisition is costly and sparse.

Related papers

• Accurate Network Traffic Matrix Prediction via LEAD: a Large Language Model-Enhanced Adapter-Based Conditional Diffusion Model [41.23125529149133]
  We present LEAD, a conditional Diffusion model that transforms traffic matrices into RGB images.<n>We also propose a Dual-Conditioning Strategy to guide a diffusion model to generate complex, dynamic network traffic.<n>Experiments on the Abilene and GEANT datasets demonstrate that LEAD outperforms all baselines.
  arXiv  Detail & Related papers  (2026-01-29T09:16:05Z)
• Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets [34.15484094708584]
  We develop a physics-inspired graph neural network (GNN) surrogate that operates directly on unstructured meshes.<n>We employ transfer learning to fine-tune models across velocities, accelerating training and improving accuracy in data-limited settings.<n>Our results demonstrate that graph-based DD, combined with transfer learning, provides a scalable and reliable pathway for training GNN surrogates on massive PDE-governed systems.
  arXiv  Detail & Related papers  (2025-12-01T17:10:09Z)
• Physics-informed Neural Operator Learning for Nonlinear Grad-Shafranov Equation [18.564353542797946]
  In magnetic confinement nuclear fusion, rapid and accurate solution of the Grad-Shafranov equation (GSE) is essential for real-time plasma control and analysis.<n>Traditional numerical solvers achieve high precision but are computationally prohibitive, while data-driven surrogates infer quickly but fail to enforce physical laws and generalize poorly beyond training distributions.<n>We present a Physics-Informed Neural Operator (PINO) that directly learns the GSE solution operator, mapping shape parameters of last closed flux surface to equilibrium solutions for realistic nonlinear current profiles.
  arXiv  Detail & Related papers  (2025-11-24T13:46:38Z)
• MeanFlow Transformers with Representation Autoencoders [71.45823902973349]
  MeanFlow (MF) is a diffusion-motivated generative model that enables efficient few-step generation by learning long jumps directly from noise to data.<n>We develop an efficient training and sampling scheme for MF in the latent space of a Representation Autoencoder (RAE)<n>We achieve a 1-step FID of 2.03, outperforming vanilla MF's 3.43, while reducing sampling GFLOPS by 38% and total training cost by 83% on ImageNet 256.
  arXiv  Detail & Related papers  (2025-11-17T06:17:08Z)
• DiffusionNFT: Online Diffusion Reinforcement with Forward Process [99.94852379720153]
  Diffusion Negative-aware FineTuning (DiffusionNFT) is a new online RL paradigm that optimize diffusion models directly on the forward process via flow matching.<n>DiffusionNFT is up to $25times$ more efficient than FlowGRPO in head-to-head comparisons, while being CFG-free.
  arXiv  Detail & Related papers  (2025-09-19T16:09:33Z)
• Neural Approximators for Low-Thrust Trajectory Transfer Cost and Reachability [1.9020115730627583]
  This paper proposes general-purpose pretrained neural networks to predict low-thrust trajectory metrics.<n>The data are transformed into a self-similar space, enabling the neural network to adapt to arbitrary semi-major axes, inclinations, and central bodies.<n>The resulting neural network achieves a relative error of 0.78% in predicting velocity increments and 0.63% in minimum transfer time estimation.
  arXiv  Detail & Related papers  (2025-08-04T21:25:46Z)
• Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models [0.0]
  This work demonstrates a proof-of-principle for using uncertainty-aware architectures to construct efficient datasets for surrogate model generation.<n>This strategy was applied again to the plasma turbulent transport problem within tokamak fusion plasmas, specifically the QuaLiKiz quasilinear electrostatic gyrokinetic turbulent transport code.<n>With 45 active learning iterations, moving from a small initial training set of $102$ to a final set of $104$, the resulting models reached a $F_1$ classification performance of 0.8 and a $R2$ regression performance of 0.75 on an independent test set
  arXiv  Detail & Related papers  (2025-07-21T18:15:12Z)
• FORT: Forward-Only Regression Training of Normalizing Flows [85.66894616735752]
  We revisit classical normalizing flows as one-step generative models with exact likelihoods.<n>We propose a novel, scalable training objective that does not require computing the expensive change of variable formula used in conventional maximum likelihood training.
  arXiv  Detail & Related papers  (2025-06-01T20:32:27Z)
• A Multi-Fidelity Graph U-Net Model for Accelerated Physics Simulations [1.2430809884830318]
  We propose a novel GNN architecture, Multi-Fidelity U-Net, that utilizes the advantages of the multi-fidelity methods for enhancing the performance of the GNN model.<n>We show that the proposed approach performs significantly better in accuracy and data requirement.<n>We also present Multi-Fidelity U-Net Lite, a faster version of the proposed architecture, with 35% faster training, with 2 to 5% reduction in accuracy.
  arXiv  Detail & Related papers  (2024-12-19T20:09:38Z)
• FlowTS: Time Series Generation via Rectified Flow [67.41208519939626]
  FlowTS is an ODE-based model that leverages rectified flow with straight-line transport in probability space.<n>For unconditional setting, FlowTS achieves state-of-the-art performance, with context FID scores of 0.019 and 0.011 on Stock and ETTh datasets.<n>For conditional setting, we have achieved superior performance in solar forecasting.
  arXiv  Detail & Related papers  (2024-11-12T03:03:23Z)
• Just How Flexible are Neural Networks in Practice? [89.80474583606242]
  It is widely believed that a neural network can fit a training set containing at least as many samples as it has parameters. In practice, however, we only find solutions via our training procedure, including the gradient and regularizers, limiting flexibility.
  arXiv  Detail & Related papers  (2024-06-17T12:24:45Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams [52.77024349608834]
  We classify transient noise signals (i.e.glitches) and gravitational waves in data from the Advanced LIGO detectors. We use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset. We also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels.
  arXiv  Detail & Related papers  (2023-03-24T11:12:37Z)
• Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns [77.34726150561087]
  We introduce four complicated missing patterns, including missing and three fiber-like missing cases according to the mode-drivenn fibers. Despite nonity of the objective function in our model, we derive the optimal solutions by integrating alternating data-mputation method of multipliers.
  arXiv  Detail & Related papers  (2022-05-19T08:37:56Z)
• Transfer Learning Based Efficient Traffic Prediction with Limited Training Data [3.689539481706835]
  Efficient prediction of internet traffic is an essential part of Self Organizing Network (SON) for ensuring proactive management. Deep sequence model in network traffic prediction with limited training data has not been studied extensively in the current works. We investigated and evaluated the performance of the deep transfer learning technique in traffic prediction with inadequate historical data.
  arXiv  Detail & Related papers  (2022-05-09T14:44:39Z)
• Robust Learning via Persistency of Excitation [4.674053902991301]
  We show that network training using gradient descent is equivalent to a dynamical system parameter estimation problem. We provide an efficient technique for estimating the corresponding Lipschitz constant using extreme value theory. Our approach also universally increases the adversarial accuracy by 0.1% to 0.3% points in various state-of-the-art adversarially trained models.
  arXiv  Detail & Related papers  (2021-06-03T18:49:05Z)

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.