Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models

• URL: http://arxiv.org/abs/2507.15976v1
• Date: Mon, 21 Jul 2025 18:15:12 GMT
• Title: Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models
• Abstract summary: 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
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work demonstrates a proof-of-principle for using uncertainty-aware architectures, in combination with active learning techniques and an in-the-loop physics simulation code as a data labeller, to construct efficient datasets for data-driven surrogate model generation. Building off of a previous proof-of-principle successfully demonstrating training set reduction on static pre-labelled datasets, using the ADEPT framework, this strategy was applied again to the plasma turbulent transport problem within tokamak fusion plasmas, specifically the QuaLiKiz quasilinear electrostatic gyrokinetic turbulent transport code. While QuaLiKiz provides relatively fast evaluations, this study specifically targeted small datasets to serve as a proxy for more expensive codes, such as CGYRO or GENE. The newly implemented algorithm uses the SNGP architecture for the classification component of the problem and the BNN-NCP architecture for the regression component, training models for all turbulent modes (ITG, TEM, ETG) and all transport fluxes ($Q_e$, $Q_i$, $\Gamma_e$, $\Gamma_i$, and $\Pi_i$) described by the general QuaLiKiz output. With 45 active learning iterations, moving from a small initial training set of $10^{2}$ to a final set of $10^{4}$, the resulting models reached a $F_1$ classification performance of ~0.8 and a $R^2$ regression performance of ~0.75 on an independent test set across all outputs. This extrapolates to reaching the same performance and efficiency as the previous ADEPT pipeline, although on a problem with 1 extra input dimension. While the improvement rate achieved in this implementation diminishes faster than expected, the overall technique is formulated with components that can be upgraded and generalized to many surrogate modeling applications beyond plasma turbulent transport predictions.

Related papers

• Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport [0.0]
  This chapter reviews advances in Scientific Machine Learning (SciML) for modeling coupled fluid flow and transport phenomena.<n>The chapter surveys state-of-the-art SciML methods for building efficient surrogate models.<n>It shows how SciML enables fast, accurate approximations of complex coupled systems.
  arXiv  Detail & Related papers  (2026-06-17T20:02:13Z)
• Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration [71.77541419885925]
  We present a unified graph-based DP framework.<n>By modeling the dataset as a weighted graph, DP can be cast as a Maximum Weight Clique Problem (MWCP)<n>Our method outperforms existing DP methods while substantially reducing training cost, reducing training time by over 40% without sacrificing accuracy on ImageNet-1k with ResNet-50.
  arXiv  Detail & Related papers  (2026-06-11T05:13:32Z)
• Machine Learning Models to Identify Promising Nested Antiresonance Nodeless Fiber Designs [0.0]
  We propose a machine learning framework designed to identify high-performance nested antiresonance nodeless fibers (NANFs)<n>We show that small data sets are sufficient for stable, high-accuracy performance prediction, enabling the exploration of design spaces as large as $14e6$ cases.
  arXiv  Detail & Related papers  (2026-03-02T14:13:59Z)
• Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration [67.12978375116599]
  We show that the steps of gradient descent (GD) reduce to those of generalized perceptron algorithms.<n>This helps explain the optimization dynamics and the implicit acceleration phenomenon observed in neural networks.
  arXiv  Detail & Related papers  (2025-12-12T14:16:35Z)
• Exploiting Function-Family Structure in Analog Circuit Optimization [22.334314861424943]
  Off-the-shelf Gaussian-process surrogates impose globally smooth, stationary priors that are misaligned with these regime-switching primitives.<n>We demonstrate that pre-trained models encoding these primitives enable reliable optimization without per-circuit engineering.
  arXiv  Detail & Related papers  (2025-11-30T03:21:13Z)
• EcoSpa: Efficient Transformer Training with Coupled Sparsity [79.5008521101473]
  Transformers have become the backbone of modern AI, yet their high computational demands pose critical system challenges.<n>We introduce EcoSpa, an efficient structured sparse training method that jointly evaluates and sparsifies coupled weight matrix pairs.
  arXiv  Detail & Related papers  (2025-11-09T11:23:43Z)
• Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators [2.504298819189614]
  Graph network-based simulators (GNS) have demonstrated strong potential for learning particle-based physics.<n>Existing models are typically trained for a single material type and fail to generalize across distinct behaviors.<n>We propose a parameter-efficient conditioning mechanism that makes the GNS model adaptive to material parameters.
  arXiv  Detail & Related papers  (2025-11-07T17:55:35Z)
• Elastic ViTs from Pretrained Models without Retraining [74.5386166956142]
  Vision foundation models achieve remarkable performance but are only available in a limited set of pre-determined sizes.<n>We introduce SnapViT: Single-shot network approximation for pruned Vision Transformers.<n>Our approach efficiently combines gradient information with cross-network structure correlations, approximated via an evolutionary algorithm.
  arXiv  Detail & Related papers  (2025-10-20T16:15:03Z)
• TGLF-SINN: Deep Learning Surrogate Model for Accelerating Turbulent Transport Modeling in Fusion [18.028061388104963]
  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.
  arXiv  Detail & Related papers  (2025-09-07T09:36:51Z)
• Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems [0.0]
  Real-time particle transverse momentum ($p_T$) estimation in high-energy physics demands efficient algorithms under strict hardware constraints.<n>We propose a physics-informed graph neural network (GNN) framework that systematically encodes detector geometry and physical observables.<n>Our co-design methodology yields superior accuracy-efficiency trade-offs compared to existing baselines.
  arXiv  Detail & Related papers  (2025-07-25T12:19:57Z)
• Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning [0.0]
  Extreme Learning Machine (ELM) is a lightweight, non-gradient-based machine learning algorithm for accelerating complex physical models.<n>We evaluate ELM surrogate models in two test cases with different data structures.
  arXiv  Detail & Related papers  (2025-06-24T14:46:23Z)
• Ultra-Resolution Adaptation with Ease [62.56434979517156]
  We propose a set of key guidelines for ultra-resolution adaptation termed emphURAE.<n>We show that tuning minor components of the weight matrices outperforms widely-used low-rank adapters when synthetic data are unavailable.<n>Experiments validate that URAE achieves comparable 2K-generation performance to state-of-the-art closed-source models like FLUX1.1 [Pro] Ultra with only 3K samples and 2K iterations.
  arXiv  Detail & Related papers  (2025-03-20T16:44:43Z)
• Training-free Ultra Small Model for Universal Sparse Reconstruction in Compressed Sensing [39.36305648162564]
  This paper proposes ultra-small artificial neural models called coefficients learning (CL)<n>CL enables training-free and rapid sparse reconstruction while inheriting the generality and interpretability of traditional iterative methods.<n>Compared to representative iterative methods, CLOMP improves efficiency by 100 to 1000 folds for large-scale data.
  arXiv  Detail & Related papers  (2025-01-20T16:50:59Z)
• 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)
• Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling [0.0]
  Convolutional neural networks (CNNs) have gained popularity as the basis for such surrogate models.<n>We propose training a CNN surrogate model on a mixture of numerical solutions to both the $d$-dimensional problem and its ($d-1$)-dimensional approximation.<n>We demonstrate our approach on a multiphase flow test problem, using transfer learning to train a dense fully-convolutional encoder-decoder CNN on the two classes of data.
  arXiv  Detail & Related papers  (2024-10-16T05:07:48Z)
• Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
  We identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis. We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
  arXiv  Detail & Related papers  (2024-03-26T16:33:12Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Training Energy-Based Normalizing Flow with Score-Matching Objectives [36.0810550035231]
  We present a new flow-based modeling approach called energy-based normalizing flow (EBFlow) We demonstrate that by optimizing EBFlow with score-matching objectives, the computation of Jacobian determinants for linear transformations can be entirely bypassed.
  arXiv  Detail & Related papers  (2023-05-24T15:54:29Z)
• Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design [68.1682448368636]
  We present a supervised pretraining approach to learn circuit representations that can be adapted to new unseen topologies or unseen prediction tasks. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties.
  arXiv  Detail & Related papers  (2022-03-29T21:18:47Z)
• AutoSimulate: (Quickly) Learning Synthetic Data Generation [70.82315853981838]
  We propose an efficient alternative for optimal synthetic data generation based on a novel differentiable approximation of the objective. We demonstrate that the proposed method finds the optimal data distribution faster (up to $50times$), with significantly reduced training data generation (up to $30times$) and better accuracy ($+8.7%$) on real-world test datasets than previous methods.
  arXiv  Detail & Related papers  (2020-08-16T11:36:11Z)

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.