Nathaniel Chen

研究。

托卡馬克的即時控制——基礎模型、電腦視覺,以及用於電漿狀態估測與不穩定性預測的訊號處理。指導教授為 Princeton MAE / PPPL 的 Egemen Kolemen。

研究

TokEye

Find the structure hiding in a noisy signal.

A tool for extracting coherent and transient features from fluctuating time series—turning a crowded spectrogram into something a researcher can measure.

OracleFlow

How much better could a model still get?

A controlled setting for studying learning: separate ambiguity in the data from the error a model could still reduce.

Fusion World Model

Learn how a plasma evolves. Help shape what happens next.

A world model for fusion, built from scratch to connect diagnostic measurements, learn plasma dynamics, and support better experimental decisions.

DIII-D control

Turn a camera measurement into a control decision.

Image-based feedback for divertor detachment at DIII-D: connecting what the camera sees to how an experiment responds.

Fig. 01 · TokEye——針對波動性托卡馬克時間序列的快速訊號擷取。
Princeton UniversityPrinceton Plasma Physics LaboratoryDIII-D National Fusion FacilityGeneral AtomicsKSTAR / KFEUCLANYUUCSD Institute for Engineering MedicineScripps Institution of Oceanography

著作

著作
13
引用
9
合作機構
9
Towards Large-Scale Heterogeneous Data Organization for Scientific Foundation Models: A Nuclear Fusion Case Study
arXiv preprint arXiv:2608.275782026
Robust control of ECH deposition profiles on DIII-D
arXiv preprint arXiv:2606.136612026引用 1
Compact Experimental Negative TriAngUlarity Reactor (CENTAUR): Design Study for a Breakeven Negative Triangularity Device
67th Annual Meeting of the APS Division of Plasma Physics2026[預印本]引用 1
Regulation compliant AI for fusion: explainable image-based feedback control of divertor detachment in DIII-D tokamak
Nuclear Fusion 66 (3), 0360142026引用 6
TokEye: Fast Signal Extraction for Fluctuating Time Series via Offline Self-Supervised Learning From Fusion Diagnostics to Bioacoustics
arXiv preprint arXiv:2602.203172026
Beyond the Loss Curve: Scaling Laws, Active Learning, and the Limits of Learning from Exact Posteriors
arXiv preprint arXiv:2602.003152026
Real-time feedback control of radiation front-based detachment enabled by machine learning on DIII-D and KSTAR
67th Annual Meeting of the APS Division of Plasma Physics2025引用 1
Real-Time Machine-Learning Enabled Emission Front Control at DIII-D
Bulletin of the American Physical Society2024
Visual Perception of 3D Space and Shape in Time-Part IV-3D Shape Recognition by 3D Rotation
bioRxiv, 2022.03. 01.4821642022
What Lies Beneath the Curve? Scaling Laws in the Presence of Exact Posteriors
ICLR 2026 2nd Workshop on Deep Generative Model in Machine Learning: Theory …
Dynamic modeling of Alfvén eigenmodes using Machine Learning on DIII-D
67th Annual Meeting of the APS Division of Plasma Physics
Self-Supervised Identification of Coherent Modes in Tokamaks
67th Annual Meeting of the APS Division of Plasma Physics
Towards a Foundation Model for Fusion: Multimodal Representation Learning of Plasma State and Control
67th Annual Meeting of the APS Division of Plasma Physics

同步自 Google Scholar · 2026年9月8日