Original Article
2021, Choi, S., Woo, J., Kim, J., Choi, W., Kim, J. and Lee, J.Y., “A Survey and Analysis of Research on Digital Twin in Korea,” (in Korean) J. Korean Soc. Comput. Des. Eng., Vol.26, No.1, pp.59-69.
10.7315/CDE.2021.0592024, Kang, G., Kim, S., Cha, K., Kim, J.-H., Lee, M. and Lee, J., “Real-time Performance Prediction of a Physics-based Pump System Simulation Using Bayesian Optimization,” (in Korean) J. Comput. Fluids Eng., Vol.29, No.1, pp.113-129.
10.6112/kscfe.2024.29.1.1132022, Lee, J., Kim, G. and Oh, J., “Development of an Integrated Flow Analysis Physical Model Including a Generator Model for an Oscillating Water Column Type Wave Power Generator,” (in Korean) J. Comput. Fluids Eng., Vol.27, No.4, pp.99-106.
10.6112/kscfe.2022.27.4.0992022, Lee, W. and Lee, S., “Development of an Automated Framework for Constructing a Reduced-Order Model Based on Proper Orthogonal Decomposition and Artificial Neural Network for Real-time Fluid Motion Simulator,” (in Korean) J. Comput. Fluids Eng., Vol.27, No.4, pp.20-27.
2003, Evensen, G., “The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation,” Ocean Dynamics, Vol.53, No.4, pp.343-367.
10.1007/s10236-003-0036-92020, Raissi, M., Yazdani, A. and Karniadakis, G.E., “Hidden Fluid Mechanics: Learning Velocity and Pressure Fields from Flow Visualizations,” Science, Vol.367, No.6481, pp.1026-1030.
10.1126/science.aaw474132001523PMC72190832021, Krishnapriyan, A.S., Gholami, A., Zhe, S., Kirby, R.M. and Mahoney, M.W., “Characterizing Possible Failure Modes in Physics-Informed Neural Networks,” Adv. Neural Inf. Process. Syst., Vol.32, pp.26548-26560.
1995, Everson, R. and Sirovich, L., “Karhunen–Loève Procedure for Gappy Data,” J. Opt. Soc. Am. A, Vol.12, No.8, p.1657.
2004, Bui-Thanh, T., Damodaran, M. and Willcox, K., “Aerodynamic Data Reconstruction and Inverse Design Using Proper Orthogonal Decomposition,” AIAA J., Vol.42, No.8, pp.1505-1516.
10.2514/1.21592004, Venturi, D. and Karniadakis, G.E., “Gappy Data and Reconstruction Procedures for Flow Past a Cylinder,” J. Fluid Mech., Vol.519, pp.315-336.
10.1017/S00221120040013382006, Willcox, K., “Unsteady Flow Sensing and Estimation via the Gappy Proper Orthogonal Decomposition,” Comput. Fluids, Vol.35, No.2, pp.208-226.
10.1016/j.compfluid.2004.11.0062016, Saini, P., Arndt, C.M. and Steinberg, A.M., “Development and Evaluation of Gappy-POD as a Data Reconstruction Technique for Noisy PIV Measurements in Gas Turbine Combustors,” Exp. Fluids, Vol.57, No.7, p.122.
10.1007/s00348-016-2208-72021, Aversano, G., Ferrarotti, M. and Parente, A., “Digital Twin of a Combustion Furnace Operating in Flameless Conditions: Reduced-Order Model Development from CFD Simulations,” Proc. Combust. Inst., Vol.38, No.4, pp.5373-5381.
10.1016/j.proci.2020.06.0452024, Procacci, A., Donato, L., Amaduzzi, R., Galletti, C., Coussement, A. and Parente, A., “Parameter Estimation Using a Gaussian Process Regression-Based Reduced-Order Model and Sparse Sensing: Application to a Methane/Air Lifted Jet Flame,” Flow Turbul. Combust., Vol.112, No.3, pp.879-895.
10.1007/s10494-023-00446-x2003, Borée, J., “Extended Proper Orthogonal Decomposition: A Tool to Analyse Correlated Events in Turbulent Flows,” Exp. Fluids, Vol.35, No.2, pp.188-192.
10.1007/s00348-003-0656-32023, Lu, S. and Papadakis, G., “Flow Reconstruction Around a Surface-Mounted Prism from Sparse Velocity and/or Scalar Measurements Using a Combination of POD and a Data-Driven Estimator,” Flow Turbul. Combust., Vol.110, No.4, pp.1059-1090.
10.1007/s10494-023-00417-2- Publisher :Korean Society for Computational Fluids Engineering
- Publisher(Ko) :한국전산유체공학회
- Journal Title :Journal of Computational Fluids Engineering
- Journal Title(Ko) :한국전산유체공학회지
- Volume : 31
- No :2
- Pages :137-152
- Received Date : 2026-06-02
- Revised Date : 2026-06-15
- Accepted Date : 2026-06-18
- DOI :https://doi.org/10.6112/kscfe.2026.31.2.137
Journal of Computational Fluids Engineering
