Nathaniel Chen

研究。

トカマクのリアルタイム制御 — 基盤モデル、コンピュータビジョン、プラズマ状態推定と不安定性予測のための信号処理。指導教員は Princeton MAE / PPPL の 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— 揺らぎを含むトカマク時系列のための高速な信号抽出。
Princeton UniversityPrinceton Plasma Physics LaboratoryDIII-D National Fusion FacilityGeneral AtomicsKSTAR / KFEUCLANYUUCSD Institute for Engineering MedicineScripps Institution of Oceanography

業績

業績
18
被引用
15
共同研究機関
9
IGNITE Tokamak World Model Architecture
arXiv preprint arXiv:2610.025152026
Is Your AI Fast Enough to Run a Fusion Reactor?
arXiv preprint arXiv:2610.008452026
Compact experimental negative triangularity reactor (centaur): a design study for a compact, affordable breakeven tokamak
Plasma Physics and Controlled Fusion 68 (9), 0950252026被引用 2
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被引用 1
Regulation compliant AI for fusion: explainable image-based feedback control of divertor detachment in DIII-D tokamak
Nuclear Fusion 66 (3), 0360142026被引用 9
TokEye: Fast Signal Extraction for Fluctuating Time Series via Offline Self-Supervised Learning From Fusion Diagnostics to Bioacoustics
arXiv preprint arXiv:2602.203172026被引用 1
Beyond the Loss Curve: Scaling Laws, Active Learning, and the Limits of Learning from Exact Posteriors
arXiv preprint arXiv:2602.003152026被引用 1
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被引用 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
AI-Driven Advances Toward Intelligent Tokamak Operation
From camera-based to AI-based measurement: Radiation front-based detachment control in KSTAR and DIII-D
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
Compact Experimental Negative TriAngUlarity Reactor (CENTAUR): Design Study for a Breakeven Negative Triangularity Device
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

Google Scholar と同期 · 2026年10月25日