| Combustion machine learning: Principles, progress and prospects M Ihme, WT Chung, AA Mishra Progress in Energy and Combustion Science 91, 101010, 2022 | 148 | 2022 |
| A review of physics-informed machine learning in fluid mechanics P Sharma, WT Chung, B Akoush, M Ihme Energies 16 (5), 2343, 2023 | 52 | 2023 |
| Data-assisted combustion simulations with dynamic submodel assignment using random forests WT Chung, AA Mishra, N Perakis, M Ihme Combustion and Flame 227, 172-185, 2021 | 47 | 2021 |
| Examination of diesel spray combustion in supercritical ambient fluid using large-eddy simulations WT Chung, PC Ma, M Ihme International Journal of Engine Research 21 (1), 122-133, 2020 | 25 | 2020 |
| Interpretable data-driven methods for subgrid-scale closure in LES for transcritical LOX/GCH4 combustion WT Chung, AA Mishra, M Ihme Combustion and Flame 239, 111758, 2022 | 22 | 2022 |
| BLASTNet: A call for community-involved big data in combustion machine learning WT Chung, KS Jung, JH Chen, M Ihme Applications in Energy and Combustion Science, 100087, 2022 | 11 | 2022 |
| Analysis of ducted fuel injection at high-pressure transcritical conditions using large-eddy simulations J Guo, D Brouzet, WT Chung, M Ihme International Journal of Engine Research 25 (2), 305-319, 2024 | 7 | 2024 |
| LES of HCCI combustion of iso-octane/air in a flat-piston rapid compression machine WT Chung, N Ly, M Ihme Proceedings of the Combustion Institute 39 (4), 5309-5317, 2023 | 5 | 2023 |
| BLASTNet simulation dataset. WT Chung, M Ihme, KS Jung, JH Chen, J Guo, D Brouzet, M Talei https://blastnet.github.io/, 2022 | 5* | 2022 |
| The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning WT Chung, KS Jung, J Chen, M Ihme ICML 2022 2nd AI for Science Workshop, 2022 | 4 | 2022 |
| Random forests for Accelerating Turbulent Combustion Simulations WT Chung, A Mishra, N Perakis, M Ihme NeurIPS 2020 Machine Learning and the Physical Sciences Workshop, 2020 | 4 | 2020 |
| Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data WT Chung, B Akoush, P Sharma, A Tamkin, KS Jung, JH Chen, J Guo, ... Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023 | 3 | 2023 |
| ML4LM: Machine Learning for Safely Landing on Mars DD Wu, WT Chung, M Ihme NeurIPS 2022 Machine Learning and the Physical Sciences Workshop, 2022 | 1 | 2022 |
| Predictions of instantaneous temperature fields in jet-in-hot-coflow flames using a multi-scale U-Net model JAC Kildare, WT Chung, MJ Evans, ZF Tian, PR Medwell, M Ihme Proceedings of the Combustion Institute 40 (1-4), 105330, 2024 | | 2024 |
| Ensemble predictions of laser ignition with a hybrid stochastic physics-embedded deep-learning framework WT Chung, C Laurent, D Passiatore, M Ihme Proceedings of the Combustion Institute 40 (1-4), 105304, 2024 | | 2024 |
| Examining diesel-spray assisted ignition of ammonia under reactivity-controlled conditions using large-eddy simulations P Sharma, D Brouzet, WT Chung, M Ihme Proceedings of the Combustion Institute 40 (1-4), 105317, 2024 | | 2024 |
| Augmenting filtered flame front displacement models for LES using machine learning with a posteriori simulations JZ Ho, M Talei, D Brouzet, WT Chung, P Sharma, M Ihme Proceedings of the Combustion Institute 40 (1-4), 105311, 2024 | | 2024 |
| Fostering Open-source Resources and Practices within Deep Learning of Flow Physics WT Chung, B Akoush, P Sharma, M Ihme Bulletin of the American Physical Society, 2023 | | 2023 |
| Leveraging Local Scale Deep Autoencoder-based Models to Improve Early Time Predictions in Global Atmospheric Transport MG Fernandez, WT Chung, DD Lucas Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023 | | 2023 |
| Deep Spatiotemporal Autoencoder-Based Models for Atmospheric Transport MG Fernández-Godino, WT Chung, DD Lucas, M Ihme AGU23, 2023 | | 2023 |
Matthias IhmeStanford UniversityVerified email at stanford.edu
