| Multi-objective optimization and comparison of surrogate models for separation performances of cyclone separator based on CFD, RSM, GMDH-neural network, back propagation-ANN … D Park, J Cha, M Kim, JS Go Engineering Applications of Computational Fluid Mechanics 14 (1), 180-201, 2020 | 50 | 2020 |
| A generalizable and interpretable deep learning model to improve the prediction accuracy of strain fields in grid composites D Park, J Jung, GX Gu, S Ryu Materials & Design 223, 111192, 2022 | 20 | 2022 |
| High‐performance piezoelectric yarns for artificial intelligence‐enabled wearable sensing and classification D Kim, Z Yang, J Cho, D Park, DH Kim, J Lee, S Ryu, SW Kim, M Kim EcoMat 5 (8), e12384, 2023 | 19 | 2023 |
| Design of cyclone separator critical diameter model based on machine learning and cfd D Park, JS Go Processes 8 (11), 1521, 2020 | 18 | 2020 |
| Assessment and Calibration of a Low-Cost PM2.5 Sensor Using Machine Learning (HybridLSTM Neural Network): Feasibility Study to Build an Air Quality … D Park, GW Yoo, SH Park, JH Lee Atmosphere 12 (10), 1306, 2021 | 13 | 2021 |
| Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review J Lee, D Park, M Lee, H Lee, K Park, I Lee, S Ryu Materials Horizons, 2023 | 10 | 2023 |
| Double generative network (DGNet) pipeline for structure-property relation of digital composites D Park, J Jung, S Ryu Composite Structures 319, 117131, 2023 | 3 | 2023 |
| Feasibility evaluation of computational fluid dynamics approach for inhalation exposure assessment: Case study for biocide spray D Park, JH Lee Applied Sciences 11 (2), 634, 2021 | 3 | 2021 |
| Research of cyclone optimization based on CFD, gmdh-type neural network and genetic algorithm D Park Proceedings of the 2019 3rd International conference on virtual and …, 2019 | 3 | 2019 |
| A generalizable and interpretable deep supervised neural network to predict strain field of composite in unseen design space D Park, J Jung, G Gu, S Ryu Available at SSRN 4164581, 0 | 2 | |
| Optimization of grid composite configuration to maximize toughness using integrated hierarchical deep neural network and genetic algorithm J Lee, D Park, K Park, H Song, TS Kim, S Ryu Materials & Design 238, 112700, 2024 | 1 | 2024 |
| Hierarchical Generative Network: A Hierarchical Multitask Learning Approach for Accelerated Composite Material Design and Discovery D Park, J Lee, K Park, S Ryu Advanced Engineering Materials 25 (21), 2300867, 2023 | 1 | 2023 |
| Deep generative spatiotemporal learning for integrating fracture mechanics in composite materials: inverse design, discovery, and optimization D Park, J Lee, H Lee, GX Gu, S Ryu Materials Horizons 11 (13), 3048-3065, 2024 | | 2024 |
| Improving ALD Coating Uniformity via Computational Fluid Dynamics and Experimental Method D Park, C Kwon, Y Ji, S Ryu 한국표면공학회 학술발표회 초록집, 361-361, 2023 | | 2023 |
| Expanding Design Spaces in Digital Composite Materials: A Multi‐Input Deep Learning Approach Enhanced by Transfer Learning and Multi‐kernel Network D Park, M Park, S Ryu Advanced Theory and Simulations 6 (11), 2300465, 2023 | | 2023 |
| Prediction of Stress Distributions of Two-Phase Composite with Random Material Properties using Tailored Convolution Neural Network and Transfer Learning D Park, M Park, S Ryu 대한기계학회 춘추학술대회, 118-119, 2023 | | 2023 |
| Development of Scalable And Reliable Generation of Lipid Nanoparticles Using Automated Microfluidic System for Gene Delivery DK Jung, S Jang, D Park, NH Bae, CS Han, S Ryu, EK Lim, KG Lee Available at SSRN 4845208, 0 | | |
