| Ginns: Graph-informed neural networks for multiscale physics EJ Hall, S Taverniers, MA Katsoulakis, DM Tartakovsky Journal of Computational Physics 433, 110192, 2021 | 29 | 2021 |
| Estimation of distributions via multilevel Monte Carlo with stratified sampling S Taverniers, DM Tartakovsky Journal of Computational Physics 419, 109572, 2020 | 29 | 2020 |
| Accelerated multilevel Monte Carlo with kernel‐based smoothing and Latinized stratification S Taverniers, SBM Bosma, DM Tartakovsky Water resources research 56 (9), e2019WR026984, 2020 | 18 | 2020 |
| Noise propagation in hybrid models of nonlinear systems: The Ginzburg–Landau equation S Taverniers, FJ Alexander, DM Tartakovsky Journal of Computational Physics 262, 313-324, 2014 | 15 | 2014 |
| Mutual information for explainable deep learning of multiscale systems S Taverniers, EJ Hall, MA Katsoulakis, DM Tartakovsky Journal of Computational Physics 444, 110551, 2021 | 13 | 2021 |
| 2D particle-in-cell modeling of dielectric insulator breakdown S Taverniers, CH Lim, JP Verboncoeur 2009 IEEE International Conference on Plasma Science-Abstracts, 1-1, 2009 | 11 | 2009 |
| Conservative tightly-coupled simulations of stochastic multiscale systems S Taverniers, AY Pigarov, DM Tartakovsky Journal of Computational Physics 313, 400-414, 2016 | 9 | 2016 |
| Physics-based statistical learning approach to mesoscopic model selection S Taverniers, TS Haut, K Barros, FJ Alexander, T Lookman Physical Review E 92 (5), 053301, 2015 | 9 | 2015 |
| A tightly-coupled domain-decomposition approach for highly nonlinear stochastic multiphysics systems S Taverniers, DM Tartakovsky Journal of Computational Physics 330, 884-901, 2017 | 8 | 2017 |
| Two-way coupled Cloud-In-Cell modeling of non-isothermal particle-laden flows: A Subgrid Particle-Averaged Reynolds Stress-Equivalent (SPARSE) formulation S Taverniers, HS Udaykumar, GB Jacobs Journal of Computational Physics 390, 595-618, 2019 | 6 | 2019 |
| Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor S Taverniers, DM Tartakovsky Journal of Computational Physics 348, 139-150, 2017 | 4 | 2017 |
| A localized artificial diffusivity approach inspired by TVD schemes and its consistent application to compressible flows S Mirjalili, S Taverniers, H Collis, M Behandish, A Mani Center for Turbulence Research, Stanford University, 169-182, 2021 | 2 | 2021 |
| Inverse asymptotic treatment: capturing discontinuities in fluid flows via equation modification S Mirjalili, S Taverniers, H Collis, M Behandish, A Mani Journal of Computational Science 73, 102141, 2023 | 1 | 2023 |
| Accelerating part-scale simulation in liquid metal jet additive manufacturing via operator learning S Taverniers, S Korneev, KM Pietrzyk, M Behandish arXiv preprint arXiv:2202.03665, 2022 | 1 | 2022 |
| Graph-informed neural networks S Taverniers, EJ Hall, MA Katsoulakis, DM Tartakovsky AAAI 2021 Spring Symposium Series: Combining Artificial Intelligence and …, 2021 | 1 | 2021 |
| Machine-learning based multi-scale model for shock-particle interactions O Sen, S Taverniers, P Das, G Jacobs, HS Udaykumar Bulletin of the American Physical Society 64, 2019 | 1 | 2019 |
| Multi-scale Simulation of the Interaction of a Shock Wave and a Cloud of Particles S Taverniers, GB Jacobs, V Fountoulakis, O Sen, HS Udaykumar 31st International Symposium on Shock Waves 2: Applications 31, 467-473, 2019 | 1 | 2019 |
| Modeling of liquid-gas meniscus dynamics for arbitrary nozzle geometries S Taverniers, A Lew, S Korneev, C Somarakis, M Behandish US Patent App. 18/086,348, 2024 | | 2024 |
| Surrogate modeling of molten droplet coalescence in additive manufacturing S Taverniers, M Behandish, S Korneev US Patent App. 17/864,082, 2024 | | 2024 |
| A multi-physics compiler for generating numerical solvers from differential equations JT Maxwell III, M Behandish, S Taverniers arXiv preprint arXiv:2311.16404, 2023 | | 2023 |
Daniel M. TartakovskyStanford University在 stanford.edu 的电子邮件经过验证
