AIFR Group

物理 Physics & Fundamental Science

AIFR 的起點與核心：將機器學習應用於基礎科學研究，涵蓋量子多體物理、天文與腦科學等領域，並以同儕審查期刊論文發表成果。
Where AIFR began — applying machine learning to fundamental science, published in peer-reviewed journals.

研究計畫 Projects

量子多體 / 拓樸材料

Quantum Many-Body & Topological Material

以隨機取樣神經網路（RSNN）及自監督學習研究量子多體問題與拓樸相變的分類與辨識。

天文 — 尋找年輕恆星體

Astronomy — Young Stellar Objects

以卷積神經網路（CNN）僅依光譜能量分布（SED）分類恆星、星系與年輕恆星體，達高精確度與召回率。

腦科學 — 神經元極性辨識

Brain Science — Neuron Polarity

以節點式機器學習辨識果蠅腦神經元的軸突與樹突極性，協助大規模神經連結體研究。

相關論文 Selected Publications

Chi-Ting Ho and Daw-Wei Wang, Unsupervised Framework for Identifying Diverse Quantum Phase Transitions Using Classical Shadow Tomography, Phys. Rev. Research B 113, 155139 (2026).
Chi-Ting Ho and Daw-Wei Wang, Self-Supervised Ensemble Learning: A Universal Method for Phase Transition Classification of Many-Body Systems, Phys. Rev. Research 5, 043090 (2023).
Yang-Sheng Chao, Chen-Zhi Su, Ting-Yuan Chen, Daw-Wei Wang and Kuo-Chuan Pan, Determine the Core Structure and Nuclear Equation of State of Rotating Core-Collapse Supernovae with Gravitational Waves by Convolutional Neural Networks, Astrophys. J. 939, 13 (2022).
Chi-Ting Ho and Daw-Wei Wang, Robust Identification of Topological Phase Transition by Self-supervised Machine Learning Approach, New J. Phys. 23, 083021 (2021).
Chen-Yu Liu and Daw-Wei Wang, Random Sampling Neural Network for Quantum Many-Body Problems, Phys. Rev. B 103, 205107 (2021).
Yi-Lung Chiu, Chi-Ting Ho, Daw-Wei Wang and Shih-Ping Lai, Searching for Young Stellar Objects through SEDs by Machine Learning, Astronomy and Computing 36, 100470 (2021).
Chen-Zhi Su et al. and Daw-Wei Wang, Identification of Neuronal Polarity by Node-Based Machine Learning, Neuroinformatics 19, 669 (2021).

→ 完整論文列表 Full publication & patent list