Deep learning for topology optimization design. - Yonggyun Yu; Taeil Hur; Jaeho Jung (CoRR, 2018)

Unit Module-Based Convergence Acceleration for Topology Optimization Using the Spatiotemporal Deep Neural Network. - Younghwan Joo; Yonggyun Yu; In Gwun Jang (IEEE Access, 2021)

Quantile Autoencoder With Abnormality Accumulation for Anomaly Detection of Multivariate Sensor Data. - Seunghyoung Ryu; Jiyeon Yim; Junghoon Seo; Yonggyun Yu; Hogeon Seo (IEEE Access, 2022)

Reinforcement Learning-Based Delay Line Design for Crosstalk Minimization. - Jaeho Jung; Yonggyun Yu; Soobum Lee (IEEE Access, 2024)

Dynamic graph-based convergence acceleration for topology optimization in unstructured meshes. - Younghwan Joo; Heesun Choi; Gil-Eon Jeong; Yonggyun Yu (Eng. Appl. Artif. Intell., 2024)

Quantile-Mixer: A Novel Deep Learning Approach for Probabilistic Short-Term Load Forecasting. - Seunghyoung Ryu; Yonggyun Yu (IEEE Trans. Smart Grid, 2024)

Can Untrained Neural Networks Detect Anomalies? - Seunghyoung Ryu; Yonggyun Yu; Hogeon Seo (IEEE Trans. Ind. Informatics, 2024)