| Numerical study of variable density turbulence interaction with a normal shock wave Y Tian, FA Jaberi, Z Li, D Livescu Journal of Fluid Mechanics 829, 551-588, 2017 | 66 | 2017 |
| Data-Driven Learning for the Mori--Zwanzig Formalism: A Generalization of the Koopman Learning Framework YT Lin, Y Tian, D Livescu, M Anghel SIAM Journal on Applied Dynamical Systems 20 (4), 2558-2601, 2021 | 55 | 2021 |
| Density effects on post-shock turbulence structure and dynamics Y Tian, FA Jaberi, D Livescu Journal of Fluid Mechanics 880, 935-968, 2019 | 31 | 2019 |
| Physics-informed machine learning of the Lagrangian dynamics of velocity gradient tensor Y Tian, D Livescu, M Chertkov Physical Review Fluids 6 (9), 094607, 2021 | 27 | 2021 |
| Lagrangian Large Eddy Simulations via Physics Informed Machine Learning Y Tian, M Woodward, M Stepanov, C Fryer, C Hyett, D Livescu, ... arXiv preprint arXiv:2207.04012, 2022 | 24 | 2022 |
| Regression-based projection for learning Mori--Zwanzig operators YT Lin, Y Tian, D Livescu arXiv preprint arXiv:2205.05135, 2022 | 22 | 2022 |
| Data-driven learning of Mori–Zwanzig operators for isotropic turbulence Y Tian, YT Lin, M Anghel, D Livescu Physics of Fluids 33 (12), 125118, 2021 | 22 | 2021 |
| Numerical simulation of multi-fluid shock-turbulence interaction Y Tian, F Jaberi, D Livescu, Z Li AIP Conference Proceedings 1793 (1), 150010, 2017 | 13 | 2017 |
| Physics informed machine learning with Smoothed particle hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence M Woodward, Y Tian, C Hyett, C Fryer, D Livescu, M Stepanov, ... arXiv preprint arXiv:2110.13311, 2021 | 12 | 2021 |
| Data-Driven Mori-Zwanzig: Approaching a Reduced Order Model for Hypersonic Boundary Layer Transition M Woodward, Y Tian, AT Mohan, YT Lin, C Hader, D Livescu, HF Fasel, ... AIAA SCITECH 2023 Forum, 1624, 2023 | 10 | 2023 |
| Physics Informed Machine Learning of SPH: Machine Learning Lagrangian Turbulence MJ Woodward, Y Tian, C Hyett, C Fryer, D Livescu, M Stepanov, ... | 10 | 2021 |
| Shock propagation in media with non-uniform density Y Tian, F Jaberi, D Livescu 31st International Symposium on Shock Waves 1: Fundamentals 31, 1167-1175, 2019 | 6 | 2019 |
| Numerical study of shock–turbulence interactions in variable density flows Y Tian, F Jaberi, D Livescu, Z Li Proceedings of TSFP-10 (2017) Chicago, International Symposium on Turbulence …, 2017 | 5 | 2017 |
| Modeling of Shock Propagation in Non-uniform Density Media Y Tian, FA Jaberi, D Livescu AIAA Scitech 2020 Forum, 0101, 2020 | 4 | 2020 |
| Density Effects on the Flow Structure in Multi-fluid Shock-turbulence Interaction Y Tian, FA Jaberi, D Livescu 2018 AIAA Aerospace Sciences Meeting, 0374, 2018 | 4 | 2018 |
| Assessment of Machine Learning Classification Based Models in Identifying Reaction Occurrence in Turbulence Shockwave Interaction (STI) I Alshybani, F Jaberi, MS Murillo, Y Tian AIAA SCITECH 2023 Forum, 0595, 2023 | 3 | 2023 |
| Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning M Chertkov, Y Tian, M Stepanov, C Fryer, M Woodward, C Hyett, ... Bulletin of the American Physical Society, 2022 | 2 | 2022 |
| Machine Learning Statistical Lagrangian Geometry of Turbulence C Hyett, M Chertkov, Y Tian, D Livescu APS Division of Fluid Dynamics Meeting Abstracts, S01. 024, 2020 | 2 | 2020 |
| Shock-Turbulence Interaction in Variable Density Flows Y Tian, F Jaberi, D Livescu Modeling and Simulation of Turbulent Mixing and Reaction: For Power, Energy …, 2020 | 2 | 2020 |
| Applicability of Machine Learning Methodologies to Model the Statistical Evolution of the Coarse-Grained Velocity Gradient Tensor C Hyett, Y Tian, M Woodward, M Chertkov, D Livescu, M Stepanov Bulletin of the American Physical Society, 2022 | 1 | 2022 |
Daniel LivescuScientist, Los Alamos National LaboratoryVerified email at lanl.gov
