Publications

[J] International Journal  ·  [C] Conference Proceedings  ·  [D] Domestic  ·  [P] Preprint
Preprints
[P5]
Knowledge Graph-Based Cultural Heritage Recommendation with Condition-Aware Similarity and Zero-Shot Embedding. with Y. Cha, J. Han, J. Kim, and J. Lee.
Data Science Knowledge Graph Recommender System
[P4]
KGAGRN: Knowledge graph attention networks for large-scale gene regulatory network inference. with H. Kim and P. J. Seo.
Data Science Knowledge Graph Gene Regulatory Network
[P3]
Adaptive Sobolev training for implicit neural representations. with B. Lim and H. J. Hwang.
Physics-Informed AI Implicit Neural Representation
[P2]
Physics-Aware Tensor Field Neural PDE for Climate and Weather Prediction. with N. Cho, S. W. Cho, Y. Hong, H. J. Hwang, and J. Lee.
Physics-Informed AI Neural Operator Weather Prediction
[P1]
Sobolev acceleration for neural networks. with J. K. Oh and H. Lyu.
Physics-Informed AI Optimization
2026
[J16]
Robust identification of multiple point targets in time-domain fluorescence diffuse optical tomography. with J. Eom and J. Kim.
AIMS Mathematics, Vol. 11, No. 5: 14791–14819 (2026)
Physics-Informed AI Inverse Problem
[J15]
Enhancing marketing strategies for small businesses: developing a recommender system using offline data. with S. Cho and H. J. Hwang.
Big Data, Vol. 14 (2026)
Data Science Recommender System
[J14]
SoFoNO: Mitigating Spectral Bias in Arbitrary-Scale Image Super-Resolution via Sobolev Fourier Neural Operator. with J. K. Oh, H. J. Hwang, and J. Oh.
Neurocomputing, Vol. 661 (2026)
Physics-Informed AI Neural Operator Super-Resolution
2025
[J13]
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines. H. Son.
IEEE Access, Vol. 13 (2025)
Physics-Informed AI Neural Operator
[C1]
Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems. with S. W. Cho.
ICLR 2025
Physics-Informed AI Inverse Problem Neural Operator
[D5]
Completing Cultural Heritage Knowledge Graphs Using GraIL-Based Inductive Reasoning. with J. Han, J. Kim, and J. Lee.
ICTC 2025
Data Science Knowledge Graph
[D4]
Condition-Aware Cultural Heritage Recommender System Using Knowledge Graph. with Y. Cha, Y. Kim, J. Kim, and J. Lee.
ICTC 2025
Data Science Recommender System Knowledge Graph
[D3]
지식그래프 기반의 문화유산 추천시스템. with Sung Woong Cho, Jong Kwon Oh, Jiwon Lee.
정보과학회 컴퓨팅의 실제 학술지, 2025.02
Data Science Knowledge Graph Recommender System
2024
[J12]
A PINN Approach for identifying governing parameters of thermoacoustic oscillations. with M. Lee.
Journal of Fluid Mechanics, Vol. 984 (2024)
Physics-Informed AI Inverse Problem
[D2]
Cultural Heritage Knowledge Graph and Recommender System. with Jae Seung Kim, Jiwon Lee. ICTC 2024
ICTC 2024
Data Science Knowledge Graph Recommender System
2023
[J11]
Thermal management model for microprocessors via physics-informed neural networks. with H. Cho and H. J. Hwang.
IEEE Access, Vol. 11 (2023)
Physics-Informed AI Mathematical Modeling
[J10]
The deep minimizing movement scheme. with C. Kim, M. S. Park, and H. J. Hwang.
Journal of Computational Physics (2023), 112518
Physics-Informed AI Optimization JKO Scheme
[J9]
Sobolev training for physics-informed neural networks. with J. W. Jang, W. J. Han, and H. J. Hwang.
Communications in Mathematical Sciences, Vol. 21 (2023) (6), pp. 1679–1705
Physics-Informed AI Optimization
[J8]
Enhanced Physics-Informed Neural Networks with Augmented Lagrangian Relaxation Method (AL-PINNs). with S. W. Cho and H. J. Hwang.
Neurocomputing, Vol. 548 (2023), 126424
Physics-Informed AI Optimization
[J7]
Continuous probabilistic solution to the transient self-oscillation under stochastic forcing: a PINN approach. with M. Lee.
Journal of Mechanical Science and Technology, Vol. 37 (2023) (8)
Physics-Informed AI Inverse Problem
[D1]
물리 정보 기계학습의 발전 및 응용. with J. Lee.
전자공학회지, Vol. 50 (2023), pp. 50–59
Physics-Informed AI Survey
2022
[J6]
Lagrangian dual framework for conservative neural network solutions of kinetic equations. with H. J. Hwang.
Kinetic and Related Models, Vol. 15 (2022), pp. 551–568
Physics-Informed AI
2021
[J5]
Traveling wave solutions of partial differential equations via neural networks. with S. W. Cho and H. J. Hwang.
Journal of Scientific Computing, Vol. 89 (2021), 21
Physics-Informed AI
[J4]
Explainability of machine learning models for bankruptcy prediction. with M. S. Park, C. Hyun, and H. J. Hwang.
IEEE Access, Vol. 9 (2021), pp. 124887–124899
Data Science Explainability
2020
[J3]
Real-World implications of a rapidly responsive COVID-19 spread model with time-dependent parameters via deep learning. with H. Jo, S. Y. Jung, and H. J. Hwang.
Journal of Medical Internet Research, Vol. 22 (2020), e19907
Physics-Informed AI Data Science Epidemiology
[J2]
Deep neural network approach to forward-inverse problems. with H. Jo, H. J. Hwang, and E. H. Kim.
Networks and Heterogeneous Media, Vol. 15 (2020), pp. 247–259
Physics-Informed AI Inverse Problem
2019
[J1]
Data analytic approach for bankruptcy prediction. with C. Hyun, D. Phan, and H. J. Hwang.
Expert Systems with Applications, Vol. 138 (2019), 112816
Data Science Explainability