Papers List


PIML(Physics-informed Machine Learning)

  • Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, 2018, Journal of Computational Physics

  • Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications, 2003,

  • A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations, 2023, Journal of Computational Physics

  • Physics-informed machine learning: case studies for weather and climate modelling, 2020, Philosophical Transactions of the Royal Society

  • Discovering governing equations from data by sparseidentification of nonlinear dynamical systems, 2016, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES


Anomaly Detection

  • Anomaly Detection : A Survey, 2009, ACM Computing Surveys (CSUR)

  • A clustering algorithm-based control chart for inhomogeneously distributed TFT-LCD processes, 2013, International Journal of Production Research

  • LOF: Identifying Density-Based Local Outliers, 2000,


Machine Learning

  • Random Forests, 2001, Machine Learning

Scheduling

  • A Reinforcement Learning Approach to Robust Scheduling of Semiconductor Manufacturing Facilities, 2020, IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

  • An efficient two-stage genetic algorithm for a flexible job-shop scheduling problem with sequence dependent attached_detached setup machine release date and lag-time, 2020, Computers & Industrial Engineering

  • A Generation and Repair Approach to Scheduling Semiconductor Packaging Facilities Using Case-Based Reasoning, 2023, IEEE Access