| Graph neural ordinary differential equations M Poli, S Massaroli, J Park, A Yamashita, H Asama, J Park arXiv preprint arXiv:1911.07532, 2019 | 451 | 2019 |
| Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning J Park, J Chun, SH Kim, Y Kim, J Park International journal of production research 59 (11), 3360-3377, 2021 | 244 | 2021 |
| Dissecting neural odes S Massaroli, M Poli, J Park, A Yamashita, H Asama Advances in Neural Information Processing Systems 33, 3952-3963, 2020 | 224 | 2020 |
| Layout optimization for maximizing wind farm power production using sequential convex programming J Park, K Law Applied Energy 151, 320-334, 2015 | 164 | 2015 |
| Multi-agent actor-critic with hierarchical graph attention network H Ryu, H Shin, J Park Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7236-7243, 2020 | 135 | 2020 |
| Electromagnetic energy harvester with repulsively stacked multilayer magnets for low frequency vibrations SD Kwon, J Park, K Law Smart materials and structures 22 (5), 055007, 2013 | 133 | 2013 |
| Demand-side management with shared energy storage system in smart grid J Jo, J Park IEEE Transactions on Smart Grid 11 (5), 4466-4476, 2020 | 126 | 2020 |
| A data-driven, cooperative wind farm control to maximize the total power production J Park, KH Law Applied Energy 165, 151-165, 2016 | 126 | 2016 |
| Toward a generalized energy prediction model for machine tools R Bhinge, J Park, KH Law, DA Dornfeld, M Helu, S Rachuri Journal of manufacturing science and engineering 139 (4), 041013, 2017 | 115 | 2017 |
| Learning collaborative policies to solve np-hard routing problems M Kim, J Park Advances in Neural Information Processing Systems 34, 10418-10430, 2021 | 111 | 2021 |
| Cooperative wind turbine control for maximizing wind farm power using sequential convex programming J Park, KH Law Energy Conversion and Management 101, 295-316, 2015 | 109 | 2015 |
| Physics-induced graph neural network: An application to wind-farm power estimation J Park, J Park Energy 187, 115883, 2019 | 104 | 2019 |
| Hypergraph convolutional recurrent neural network J Yi, J Park Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 91 | 2020 |
| Wind farm power maximization based on a cooperative static game approach J Park, S Kwon, KH Law Active and Passive Smart Structures and Integrated Systems 2013 8688, 204-218, 2013 | 88 | 2013 |
| Large‐eddy simulation of stable boundary layer turbulence and estimation of associated wind turbine loads J Park, S Basu, L Manuel Wind Energy 17 (3), 359-384, 2014 | 79 | 2014 |
| Sym-nco: Leveraging symmetricity for neural combinatorial optimization M Kim, J Park, J Park Advances in Neural Information Processing Systems 35, 1936-1949, 2022 | 76 | 2022 |
| Bayesian Ascent: A Data-Driven Optimization Scheme for Real-Time Control With Application to Wind Farm Power Maximization J Park, KH Law IEEE Transactions on Control Systems Technology,, 1-14, 2016 | 72 | 2016 |
| Classification of heart sound recordings using convolution neural network H Ryu, J Park, H Shin 2016 Computing in cardiology conference (CinC), 1153-1156, 2016 | 71 | 2016 |
| An intelligent machine monitoring system for energy prediction using a Gaussian Process regression R Bhinge, N Biswas, D Dornfeld, J Park, KH Law, M Helu, S Rachuri 2014 IEEE International Conference on Big Data (Big Data), 978-986, 2014 | 64 | 2014 |
| Hypersolvers: Toward fast continuous-depth models M Poli, S Massaroli, A Yamashita, H Asama, J Park Advances in Neural Information Processing Systems 33, 21105-21117, 2020 | 60 | 2020 |
Kincho LawProfessor of Civil and Environmental Engineering, Stanford UniversityVerified email at stanford.edu