| Direct numerical simulation of the turbulent flow generated during a violent expiratory event A Fabregat, F Gisbert, A Vernet, S Dutta, K Mittal, J Pallarès Physics of Fluids 33 (3), 2021 | 67 | 2021 |
| The target-matrix optimization paradigm for high-order meshes V Dobrev, P Knupp, T Kolev, K Mittal, V Tomov SIAM Journal on Scientific Computing 41 (1), B50-B68, 2019 | 67 | 2019 |
| Reinforcement learning for adaptive mesh refinement J Yang, T Dzanic, B Petersen, J Kudo, K Mittal, V Tomov, JS Camier, ... International conference on artificial intelligence and statistics, 5997-6014, 2023 | 52 | 2023 |
| Nonconforming Schwarz-spectral element methods for incompressible flow K Mittal, S Dutta, P Fischer Computers & Fluids 191, 104237, 2019 | 43 | 2019 |
| Direct numerical simulation of turbulent dispersion of evaporative aerosol clouds produced by an intense expiratory event A Fabregat, F Gisbert, A Vernet, JA Ferré, K Mittal, S Dutta, J Pallarès Physics of Fluids 33 (3), 2021 | 38 | 2021 |
| Mesh smoothing for the spectral element method K Mittal, P Fischer Journal of Scientific Computing 78, 1152-1173, 2019 | 35 | 2019 |
| Simulation-driven optimization of high-order meshes in ALE hydrodynamics V Dobrev, P Knupp, T Kolev, K Mittal, R Rieben, V Tomov Computers & Fluids 208, 104602, 2020 | 31 | 2020 |
| Multirate timestepping for the incompressible Navier-Stokes equations in overlapping grids K Mittal, S Dutta, P Fischer Journal of Computational Physics 437, 110335, 2021 | 22 | 2021 |
| hr-Adaptivity for nonconforming high-order meshes with the target matrix optimization paradigm V Dobrev, P Knupp, T Kolev, K Mittal, V Tomov Engineering with Computers 38 (4), 3721-3737, 2022 | 21 | 2022 |
| Multi-agent reinforcement learning for adaptive mesh refinement J Yang, K Mittal, T Dzanic, S Petrides, B Keith, B Petersen, D Faissol, ... arXiv preprint arXiv:2211.00801, 2022 | 11 | 2022 |
| High-performance finite elements with MFEM J Andrej, N Atallah, JP Bäcker, JS Camier, D Copeland, V Dobrev, ... The International Journal of High Performance Computing Applications 38 (5 …, 2024 | 10 | 2024 |
| ARG-US remote area modular monitoring for dry casks and critical facilities H Tsai, B Craig, H Lee, K Mittal, Y Liu, J Shuler INMM 55th Annual Meeting, 2014 | 10 | 2014 |
| Yet another parameter-free shape optimization method KE Swartz, K Mittal, M Schmidt, JL Barrera, S Watts, DA Tortorelli Structural and Multidisciplinary Optimization 66 (12), 245, 2023 | 9 | 2023 |
| High-order mesh morphing for boundary and interface fitting to implicit geometries JL Barrera, T Kolev, K Mittal, V Tomov Computer-Aided Design 158, 103499, 2023 | 9* | 2023 |
| Impact of gaps on the flow statistics in an emergent rigid canopy P Ranjan, K Mittal, LP Chamorro, RO Tinoco Physics of Fluids 34 (6), 2022 | 9 | 2022 |
| Temperatures of Interest for the TN-32 Cask during Storage of High Burnup Fuel K Mittal, Z Han, J Li, H Tsai, Y Liu 55th Annual Institute of Nuclear Materials Management conference, 2014 | 9 | 2014 |
| Adaptive surface fitting and tangential relaxation for high-order mesh optimization P Knupp, T Kolev, K Mittal, VZ Tomov arXiv preprint arXiv:2105.12165, 2021 | 8 | 2021 |
| Accelerating high-order mesh optimization using finite element partial assembly on GPUs JS Camier, V Dobrev, P Knupp, T Kolev, K Mittal, R Rieben, V Tomov Journal of Computational Physics 474, 111808, 2023 | 7 | 2023 |
| Direct numerical simulation of rotating ellipsoidal particles using moving nonconforming Schwarz-spectral element method K Mittal, S Dutta, P Fischer Computers & Fluids 205, 104556, 2020 | 7 | 2020 |
| DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws T Dzanic, K Mittal, D Kim, J Yang, S Petrides, B Keith, R Anderson Journal of Computational Physics 506, 112924, 2024 | 6 | 2024 |
Tzanio KolevLawrence Livermore National LaboratoryVerifierad e-postadress på llnl.gov