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Minghan Chu
Observed names:
Chu, Minghan,
楚明翰
Observed institutions:
Queen's University,
Cornell University,
University of Saskatchewan,
Division of Materials Science and Engineering,
Royal Military College of Canada
ORCID:
0000-0002-5276-7209
Observed ORCID:
0000-0002-5276-7209
OpenAlex ID:
A5044044126
Quantification of Reynolds-averaged-Navier–Stokes model-form uncertainty in transitional boundary layer and airfoil flows
2022
·
Minghan Chu
, Xiaohua Wu
, et al.
·
Physics of Fluids
·
10
PDF
Model-form uncertainty quantification of Reynolds-averaged Navier–Stokes modeling of flows over a SD7003 airfoil
2022
·
Minghan Chu
, Xiaohua Wu
, et al.
·
Physics of Fluids
·
7
PDF
Kimi K2.5: Visual Agentic Intelligence
2026
·
Kimi Team
, Bai, Tongtong
, et al.
·
arXiv (Cornell University)
·
4
PDF
Uncertainty Quantification in Computational Fluid Dynamics: Physics and Machine Learning Based Approaches
2023
·
Chu, Minghan
·
arXiv (Cornell University)
·
3
PDF
Prediction of Turbulent Flow Over a Flat Plate With a Step Change From a Smooth to a Rough Surface Using a Near-Wall RANS Model
2019
·
Minghan Chu
, Donald J. Bergstrom
·
Journal of Fluids Engineering
·
3
Estimating anthropogenic heat flux by assimilating meteorological observations with a Kalman filter approach
2025
·
Yuanfeng Cui
, Minghan Chu
, et al.
·
1
A Deep Learning Approach for Epistemic Uncertainty Quantification of Turbulent Flow Simulations
2024
·
Minghan Chu
, Weicheng Qian
·
SSRN Electronic Journal
·
1
Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models
2025
·
Chu, Minghan
, Qian, Weicheng
·
arXiv (Cornell University)
PDF
Estimating anthropogenic heat flux by assimilating meteorological observations with a Kalman filter approach
2025
·
Yuanfeng Cui
, Minghan Chu
, et al.
·
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
基于近壁rans模型的光滑壁突变为粗糙壁条件下平板湍流流动预测
2025
·
楚明翰
·
中国科学院力学研究所
Convolutional Neural Networks For Turbulent Model Uncertainty Quantification
2024
·
Chu, Minghan
, Qian, Weicheng
·
arXiv (Cornell University)
PDF
Developing a Model-Consistent Reduced-Dimensionality training approach to quantify and reduce epistemic uncertainty in separated flows
2024
·
Chu, Minghan
·
arXiv (Cornell University)
PDF
Physics Based & Machine Learning Methods For Uncertainty Estimation In Turbulence Modeling
2024
·
Chu, Minghan
·
arXiv (Cornell University)
PDF
Physics Constrained Deep Learning For Turbulence Model Uncertainty Quantification
2024
·
Chu, Minghan
, Qian, Weicheng
·
arXiv (Cornell University)
PDF
Multi-fidelity Deep Learning-based methodology for epistemic uncertainty quantification of turbulence models
2023
·
Chu, Minghan
, Qian, Weicheng
·
arXiv (Cornell University)
PDF
Uncertainty Quantification For Turbulent Flows with Machine Learning
2023
·
Chu, Minghan
, Qian, Weicheng
·
arXiv (Cornell University)
PDF
Multi-Fidelity Data Assimilation For Physics Inspired Machine Learning In Uncertainty Quantification Of Fluid Turbulence Simulations
2023
·
Chu, Minghan
, Qian, Weicheng
·
arXiv (Cornell University)
PDF
Combination of Multi-Fidelity Data Sources For Uncertainty Quantification: A Lightweight CNN Approach
2023
·
Chu, Minghan
, Qian, Weicheng
·
arXiv (Cornell University)
PDF
Machine learning based uncertainty quantification of turbulence model for airfoils
2022
·
Chu, Minghan
, Qian, Weicheng
·
arXiv (Cornell University)
PDF
Prediction of an internal boundary layer on a flat plate after a step change in roughness using a near-wall RANS model
2017
·
Minghan Chu
, Fanxiao Meng
, et al.
·
Bulletin of the American Physical Society
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Works count:
23
Citation count:
29
H-index:
3
I10-index:
1
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