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
13
被引用
9
共同研究機関
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被引用 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[プレプリント]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 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
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

Google Scholar と同期 · 2026年9月8日