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English(EN) AI Surrogate Modeling for Real-Time Tokamak Equilibrium Prediction: Benchmarking Neural Architectures and Validation on EXL-50U

AI 模型在实时托卡马克等离子体预测方面进行基准测试

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个 AI 代理建模框架，用于实时预测托卡马克等离子体平衡，解决了传统 Grad-Shafranov 求解器的高计算成本问题。该研究在大型数据集上对五种神经网络架构——MLP、CNN、FNO、Transformer 和 KAN——进行了基准测试，评估了它们的准确性、效率和鲁棒性。虽然 Transformer 模型在分布内数据上实现了最高的准确性，但 CNN 在速度和可靠性方面表现出最佳的整体平衡，而 CNN 和 FNO 在未见过的数据上显示出优越的外推能力。 AI

影响 为聚变应用中的实时等离子体控制选择 AI 代理提供了实用指导。

排序理由 该集群包含一篇详细介绍用于科学应用的新 AI 方法的研究论文。

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AI 模型在实时托卡马克等离子体预测方面进行基准测试

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Guoyang Shi, Zitong Zhang, Siqi Ding, Jianguo Chen, Yapeng Zhang, Jiayi Zhi, Hanyue Zhao, Tianyuan Liu ·

    用于实时托卡马克平衡预测的人工智能代理建模：神经架构的基准测试与 EXL-50U 上的验证

    arXiv:2608.23217v1 Announce Type: cross Abstract: Fast and reliable plasma equilibrium prediction is essential for real-time tokamak operation and control, but conventional Grad-Shafranov (GS) solvers are often too costly for real-time deployment. We develop an AI surrogate frame…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    用于实时托卡马克平衡预测的人工智能代理建模：神经架构的基准测试与 EXL-50U 上的验证

    Fast and reliable plasma equilibrium prediction is essential for real-time tokamak operation and control, but conventional Grad-Shafranov (GS) solvers are often too costly for real-time deployment. We develop an AI surrogate framework and benchmark five architectures (MLP, CNN, F…

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