🎓 I will be a Ph.D. student at Cornell University in August, 2025, working with Prof. Nikolaos Bouklas. Previously, I have received B.Eng. and M.Eng. from Shanghai Jiao Tong University (SJTU). I have published an article on CMAME as the first author, the other one on IJNME as the third author, and have submitted a manuscript to npj Computational Materials.

🔫 Besides a fulfilling academic life, I relax by playing badminton🏸 or video games🎮, taking photographs using my Fujifilm📷, going to the gym🏋️, walking around the campus🚶, drinking a little🍺, and eating half of a watermelon🍉 (2kg maximum).

If you find my Chinese given name Xueling difficult to pronounce [Google translation], you can pronounce Sharrin which sounds similar. My Chinese name, which means snow (雪⛄) and elf (灵🧚), looks and sounds like a girl’s name, just like Sharrin.

Research Interest

My research interests lie in fracture phase field methods, machine learning, data-driven computational mechanics, or more general computational solid mechanics topics. My current research aims to incorporate data-driven methods into traditional mechanical analysis, such as the finite element method and fatigue life prediction methods, to complement each other.

I am truly a programming guy and sometimes even debug until 4 a.m. On my GitHub, you can find various projects I did for research and coursework that demonstrate my programming skills in computational mechanics and machine learning. Regarding computational mechanics, I have released GitHub repositories for 2D/3D linear-elastic/hyperelastic static/dynamic finite element methods using MATLAB [GitHub link] and C++ [GitHub link], 2D kinematics and dynamics using C++ [GitHub link], and 2D steady incompressible flow and Stokes flow using Python [GitHub link]. Regarding machine learning, I have open-sourced a machine learning platform named TabularEnsemble [GitHub link].

Education

• Ph.D. student, Mechanical Engineering, Cornell University
  • Starting in Aug. 2025
  • Advisor: Prof. Nikolaos Bouklas
• MEng, Computational Mechanics, Shanghai Jiao Tong University (SJTU)
  • Sept. 2022 - Mar. 2025
  • GPA: 3.82/4.0
  • Thesis: Machine-Learning-Based Fatigue Life Prediction of Fiber-Reinforced Composite Materials
  • Advisor: Prof. Lu-Wen Zhang
• BEng, Engineering Mechanics, Shanghai Jiao Tong University (SJTU)
  • Sept. 2018 - Jun. 2022
  • GPA: 3.78/4.3 (88.8/100)
  • Thesis: Data-Driven Phase Field Fracture Analysis
  • Minor: AI+X (华东五校AI+X微辅修)

Publications

• Xue-Ling Luo, Jia-Yu Ye, Pu-Song Ma, and Lu-Wen Zhang* . Data-driven enhanced phase field models for highly accurate prediction of Mode I and Mode II fracture. Computer Methods in Applied Mechanics and Engineering 400 (2022): 115535. [Publisher] [ResearchGate]

• (Submitted to npj Computational Materials) Xue-Ling Luo, Chen-Cheng Lyu, and Lu-Wen Zhang* . Physics-informed ensemble learning for robustly extrapolating and revealing fatigue life of composites.

• Pu‐Song Ma¹, Xing‐Cheng Liu¹, Xue‐Ling Luo, Shaofan Li, and Lu‐Wen Zhang* . Asymptotic homogenization of phase‐field fracture model: An efficient multiscale finite element framework for anisotropic fracture. International Journal for Numerical Methods in Engineering (2024): e7489. [Publisher] [ResearchGate]

• (In preparation) Zhichao Li, Xue-Ling Luo, Qu Chen, Jun Cai, and Jinwei Dong. Geospatial cloud computing and deep learning for large-scale infectious disease data reconstruction.

Research Experience

• Acceleration Schemes of Fracture-phase-field-based Fatigue Simulation
  • Period: Jul. 2024 - Present (as a research intern)
  • Supervisor: Prof. Emilio Martínez Pañeda (University of Oxford)
  • Role: (Will be the co-1st author) A comparative study on various acceleration methods for fracture phase field simulations of fatigue, implemented using deal.II.
• Fracture Phase Field Method in Helium Embrittlement
  • Period: Jul. 2024 - Present (as a master’s student)
  • Supervisor: Prof. Luwen Zhang (SJTU)
  • Role: (Will be the co-1st author) Propose a fracture phase field method coupled with Helium diffusion and Helium bubbles and simulate it using COMSOL.
• Physics-Informed Machine Learning for Fatigue Life Prediction
  • Period: Sept. 2022 – Present (as a master’s student)
  • Supervisor: Prof. Luwen Zhang (SJTU)
  • Role: (1st author of Publication submitted) Proposed an ensemble learning framework facilitating both empirical formulations and machine learning models (Source code and data that will be released before submission); proposed a Transformer-based model to consider the lay-up sequences of fiber-reinforced composites; constructed a benchmark platform of machine learning models for tabular prediction in Python (TabularEnsemble).
• Multiscale Fracture Phase Field Method
  • Period: Sept. 2022 – Mar. 2024 (as a bachelor’s & master’s student)
  • Supervisors: Prof. Luwen Zhang (SJTU) & Prof. Shaofan Li (UC Berkeley)
  • Role: (3rd author of Publication) Participated in the conceptualization, discussions, and revision of an asymptotic-expansion-based multiscale fracture phase field method.
• Data-driven Fracture Phase Field Method
  • Period: Sept. 2021 – Aug. 2022 (as a bachelor’s student)
  • Supervisor: Prof. Luwen Zhang (SJTU)
  • Role: (1st author of Publication) Proposed the data-driven algorithm for the fracture phase field method; implemented the proposed algorithm in MATLAB, based on a traditional 2D/3D finite element solver; validated the proposed algorithm for mode I and mode II fractures of linear elastic and neo-Hookean hyperelastic models.
• Machine Learning & Deep Learning in Geology and Public Health
  • Period: Jul. 2021 – Present (as a research intern)
  • Supervisor: Dr. Zhichao Li (Associate research fellow at Chinese Academy of Sciences)
  • Role: (2nd author of Publication in preparation) Applied the Generative Adversarial Network in imputation of temporal and spatial dengue data in Brazil; evaluated the impacts of environmental factors on the dispersion of COVID-19 from humans to animals using machine learning; applied explainable machine learning in clinical disease diagnoses.
• Morphing Airfoil Design through Topology Optimization
  • Period: Sept. 2020 - Nov. 2021 (as a undergraduate student researcher)
  • Supervisor: Prof. Wenwang Wu (SJTU)
  • Role: Performed topology optimization using ABAQUS to obtain a cellular superstructure twisting under pressure; validated the deformation behavior of the superstructure through 3D printing and experiments.
• Modeling and Force Analysis of External Piping of Aero-Engine
  • Period: Mar. 2020 - Sept. 2020 (as a undergraduate student researcher)
  • Supervisor: Prof. Yadong Wu (SJTU)
  • Role: Proposed the theoretical stress formulation of wire-wrapped pipe using elasticity theory; analyzed burst pressure values of wire-wrapping layers according to the theoretical formulation.

Honors & Awards

• National Scholarship (Top <1% nationwide, the highest honor for students in China) (Dec. 2023)
• Outstanding Teaching Assistant (Top 1%) (Sept. 2023)
• Second Prize in the China Post-Graduate Mathematical Contest in Modeling (Top 15%) (Jan. 2023)
• Excellent Undergraduate Graduation Design (Thesis) of Mechanics Major in National Universities (2/20) (Jul. 2022)
• Regal Lloyds (华高莱斯) Scholarship (2/20) (Dec. 2021)
• Changshi (常石) Scholarship (3/20) (Dec. 2020)
• Second Prize in China Undergraduate Mathematical Contest in Modeling (Top 3.6%) (Nov. 2020)
• Second Prize in Shanghai Jiao Tong University Structural Design Competition (2/56) (Nov. 2020)
• Wu Yousheng (吴有生) Scholarship (3/20) (Nov. 2019)

Teaching

• Teaching Assistant, Mechanics of Materials (Sophomore) Mar. 2024 – Jul. 2024
• Teaching Assistant, Mechanics of Materials (Sophomore) Mar. 2023 – Jul. 2023
  • Won the Outstanding Teaching Assistant (Top 1%) prize for this work
• Teaching Assistant, Theoretical Mechanics (Sophomore) Sept. 2022 – Jan. 2023

Conferences Attended

• China Computational Mechanics Conference, Dalian, China (Poster: Data-driven-based Fracture Phase Field Method) Aug. 2023
• National Symposium on Data-driven Computational Mechanics, Dalian, China Apr. 2023

Skills

• MATLAB: 2D and 3D finite element implementation with implicit and explicit solvers for the fracture phase field method
• Python: Machine learning, PyTorch/TensorFlow-based deep learning, OpenMMLab-based computer vision, and data analysis
• C++: deal.II, 2D/3D finite element implementation for beam structures, dynamics simulation of mechanical structures
• Mechanical Analysis Software: COMSOL, ABAQUS and its secondary development with Python, Ansys and Ansys Fluent, and Adams
• Auxiliary Research Skills: LaTeX, Unix, Git, Docker, Unity, Solidworks, Adobe Illustrator/Photoshop, and Visio

Languages & Tests

• Languages: English and Chinese
• TOEFL: 113 = Reading 30 + Listening 30 + Speaking 25 + Writing 28 (Jul. 2024)
• GRE: 330 = Verbal 160 + Quant 170 (May. 2024)