Sunwoong Yang

About Me 본문

About Me

Ph.D. candidate at Seoul National University in South Korea

Majoring in Aerospace Engineering

Interested in aerodynamic vehicle design using machine learning/deep learning

 

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Education

Ph.D. candidate - Seoul National University (Mar. 2020 ~ Present)

Department of Aerospace Engineering

Advisor: Prof. Kwanjung Yee

 

Master - Seoul National University (Mar. 2018 ~ Feb. 2020)

Department of Mechanical and Aerospace Engineering

Thesis: Planform Optimization of Unmanned Combat Aerial Vehicle Considering Longitudinal Stability and Low-observability Using Variable-fidelity

Advisor: Prof. Kwanjung Yee

 

Bachelor - Seoul National University (Mar. 2014 ~ Feb. 2018)

Department of Mechanical and Aerospace Engineering

 

Research Areas

Computational fluid dynamics (CFD)

  • Performance improvement of unmanned combat aerial vehicle through CFD analysis
  • Automation of the pre-processing & processing & post-processing for CFD-coupled optimization

 

Shape optimization

  • Design shape optimization of various aerodynamic vehicles
  • Multi-objective optimization for Pareto solutions

 

Bayesian optimization

  • Shape optimization considering surrogate model uncertainty

 

Data mining

  • Design rule extraction through various univariate/multivariate data mining techniques (e.g., analysis of variance, self-organizing maps, decision tree, rough set theory)

 

Surrogate modeling

  • Efficient performance prediction through various surrogate models (e.g., Gaussian process, neural networks)
  • Optimization leveraging surrogate models

 

Multi-fidelity modeling (or data fusion)

  • Efficient surrogate modeling (e.g., hierarchical kriging, multi-fidelity deep neural networks) using various data with different fidelity (i.e., low-fidelity data from coarse mesh + high-fidelity data from dense mesh)
  • Noise reduction technique considering characteristics of multi-fidelity data
  • Optimization leveraging multi-fidelity surrogate models

 

Inverse design

  • Efficient shape design with predefined target performance distribution
  • Proposal of novel inverse design optimization framework through two-step deep learning approach (multi-layer perceptron and variational autoencoder)

 

Generative modeling

  • Generative modeling in airfoil inverse design process via variational autoencoder
  • Generative modeling in the reconstruction of high-dimensional data via β-variational autoencoder with convolutional layers
  • Leveraging generative modeling for reduced-order modeling

 

Reduced-order modeling (ROM)

  • Proposal of an efficient framework for predicting high-dimensional data via β-variational autoencoder with convolutional layers
  • Comparison of various dimensionality reduction techniques (principal component analysis & autoencoder & variational autoencoder & β-variational autoencoder)

 

Feature extraction

  • Extraction of interpretable physical parameters from the given image dataset via β-variational autoencoder

 

Uncertainty quantification (UQ)

  • UQ using Gaussian process and its extension to Bayesian optimization
  • UQ using neural networks considering engineering applicability: deep ensembles
  • Calibration of uncertainty estimated by deep ensembles
  • Comparison of Gaussian process and deep ensembles considering prediction accuracy & estimated uncertainty accuracy & computational efficiency