Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks

• URL: http://arxiv.org/abs/2109.08956v1
• Date: Sat, 18 Sep 2021 15:48:02 GMT
• Title: Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks
• Authors: J. Zhu, C. Rea, R.S. Granetz, E. S. Marmar, K. J. Montes, R. Sweeney, R.A. Tinguely, D. L. Chen, B. Shen, B. J. Xiao, D. Humphreys, J. Barr, O. Meneghini
• Abstract summary: Next generation high performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. We demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Next generation high performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operation based on its low performance (LP) data is key to success. In this letter, through explorative data analysis and dedicated numerical experiments on multiple existing tokamaks, we demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor. First, our results suggest data-driven disruption predictors trained on abundant LP discharges work poorly on the HP regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Second, we find that matching operational parameters among tokamaks strongly improves cross-machine accuracy which implies our model learns from the underlying scalings of dimensionless physics parameters like q_{95}, \beta_{p} and confirms the importance of these parameters in disruption physics and cross machine domain matching from the data-driven perspective. Finally, our results show how in the absence of HP data from the target devices, the best predictivity of the HP regime for the target machine can be achieved by combining LP data from the target with HP data from other machines. These results provide a possible disruption predictor development strategy for next generation tokamaks, such as ITER and SPARC, and highlight the importance of developing on existing machines baseline scenario discharges of future tokamaks to collect more relevant disruptive data.

Related papers

• Hybrid Forecasting of Geopolitical Events [71.73737011120103]
  SAGE is a hybrid forecasting system that combines human and machine generated forecasts. The system aggregates human and machine forecasts weighting both for propinquity and based on assessed skill. We show that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data.
  arXiv  Detail & Related papers  (2024-12-14T22:09:45Z)
• Scalable and Effective Negative Sample Generation for Hyperedge Prediction [55.9298019975967]
  Hyperedge prediction is crucial for understanding complex multi-entity interactions in web-based applications. Traditional methods often face difficulties in generating high-quality negative samples due to imbalance between positive and negative instances. We present the scalable and effective negative sample generation for Hyperedge Prediction (SEHP) framework, which utilizes diffusion models to tackle these challenges.
  arXiv  Detail & Related papers  (2024-11-19T09:16:25Z)
• Generative Principal Component Regression via Variational Inference [2.4415762506639944]
  One approach to designing appropriate manipulations is to target key features of predictive models. We develop a novel objective based on supervised variational autoencoders (SVAEs) that enforces such information is represented in the latent space. We show in simulations that gPCR dramatically improves target selection in manipulation as compared to standard PCR and SVAEs.
  arXiv  Detail & Related papers  (2024-09-03T22:38:55Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Multi-Source Conformal Inference Under Distribution Shift [41.701790856201036]
  We consider the problem of obtaining distribution-free prediction intervals for a target population, leveraging multiple potentially biased data sources. We derive the efficient influence functions for the quantiles of unobserved outcomes in the target and source populations. We propose a data-adaptive strategy to upweight informative data sources for efficiency gain and downweight non-informative data sources for bias reduction.
  arXiv  Detail & Related papers  (2024-05-15T13:33:09Z)
• Comprehensive Study Of Predictive Maintenance In Industries Using Classification Models And LSTM Model [0.0]
  The study aims to delve into various machine learning classification techniques, including Support Vector Machine (SVM), Random Forest, Logistic Regression, and Convolutional Neural Network LSTM-Based, for predicting and analyzing machine performance. The primary objective of the study is to assess these algorithms' performance in predicting and analyzing machine performance, considering factors such as accuracy, precision, recall, and F1 score.
  arXiv  Detail & Related papers  (2024-03-15T12:47:45Z)
• Cross-tokamak Disruption Prediction based on Physics-Guided Feature Extraction and domain adaptation [3.0854960284180133]
  In this paper, we demonstrate a novel approach to predict disruption in a future tokamak using only a few discharges. The first step is to use the existing understanding of physics to extract physics-guided features from the diagnostic signals of each tokamak. The second step is to align a few data from the future tokamak and a large amount of data from existing tokamak.
  arXiv  Detail & Related papers  (2023-09-11T10:13:30Z)
• A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data [5.859431341476405]
  We present a predictive model based on a convolutional kernel (MiniROCKET and HYDRA) to extract features from event-log data. The proposed methodology is applied to a significant real-world collected dataset. The model was integrated into a container-based decision support system to support operators in the timely maintenance of ATMs.
  arXiv  Detail & Related papers  (2023-05-17T08:55:53Z)
• Prediction-Oriented Bayesian Active Learning [51.426960808684655]
  Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters. EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
  arXiv  Detail & Related papers  (2023-04-17T10:59:57Z)
• Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data [92.51773744318119]
  We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events. We take an outcome modeling approach to estimating HTEs, and consider how and when existing prediction models for time-to-event data can be used as plug-in estimators for potential outcomes. We theoretically analyze and empirically illustrate when and how these challenges play a role when using generic machine learning prediction models for the estimation of HTEs.
  arXiv  Detail & Related papers  (2023-02-23T14:28:55Z)
• Test-time Collective Prediction [73.74982509510961]
  Multiple parties in machine learning want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents, but may not be willing to release their data or model parameters. We explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model.
  arXiv  Detail & Related papers  (2021-06-22T18:29:58Z)
• Hybrid deep learning architecture for general disruption prediction across tokamaks [0.0]
  We present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis. The new algorithm achieves high predictive accuracy on the C-Mod, DIII-D and EAST tokamaks.
  arXiv  Detail & Related papers  (2020-07-02T21:42:00Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)
• Mixability of Integral Losses: a Key to Efficient Online Aggregation of Functional and Probabilistic Forecasts [72.32459441619388]
  We adapt basic mixable (and exponentially concave) loss functions to compare functional predictions and prove that these adaptations are also mixable (exp-concave) As an application of our main result, we prove that various loss functions used for probabilistic forecasting are mixable (exp-concave)
  arXiv  Detail & Related papers  (2019-12-15T14:25:33Z)

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.