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Peng Tsu Ann Assistant Professor (Visiting Fellow)
Peng Tsu Ann Assistant Professor (Visiting Fellow)
National University of Singapore, Singapore.
Bio:
July 2024 - Present: Peng Tsu Ann Asssitant Professor at National University of Singapore, mentored by Prof. Weizhu Bao.
Dec. 2021 - July 2024: Postdoc at University of British Columbia, supervised by Prof. Christoph Ortner.
Sep. 2016 - Dec. 2021: Ph.D at Shanghai Jiao Tong University, supervised by Prof. Lei Zhang.
Sep. 2012 - Jun. 2016: B.S at Sichuan University, supervised by Prof. Hao Wang.
Research interests:
I am broadly interested in the numerical analysis and scientific computing, especially in molecular modeling in materials science, including:
Multiscale Coupling Methods for Crystalline Defects: My research involves both a priori and a posteriori error analysis, as well as the development of adaptive algorithms for atomistic-to-continuum (A/C) and quantum mechanics/molecular mechanics (QM/MM) coupling methods, specifically for crystalline defects. Additionally, I focus on the formulation of appropriate boundary conditions for such defects.
Machine-Learned Interatomic Potentials: I am interested in the numerical analysis of MLIPs and also its various applications in materials science, integrating concepts from physics, materials science, and data science to develop a systematic approach to interatomic potential modeling. My work emphasizes understanding approximation errors, uncertainty quantifications, fine-tuning of pre-trained models, and their various applications in energy materials, soft-matter (especially coarse-grained force fields) and so on.
Scientific and Quantum Machine Learning: I study data-driven methods and (quantum) machine learning techniques—particularly neural networks—for solving partial differential equations (PDEs) and related problems in applied mathematics and physics. My research also includes uncertainty quantification, numerical algorithms and theoretical analysis of these methods.
Machine Learning in Multiscale Materials Science: I investigate the application of machine learning in modeling microstructure evolution and materials design. This includes work on optical materials, magnetic materials and other systems where multiscale behavior and data-driven techniques intersect.
Contact:
Email: yswang@nus.edu.sg
Address: S17-05-16, 10 Lower Kent Ridge Road, National University of Singapore, Singapore 119076.
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