| Multifidelity deep operator networks for data-driven and physics-informed problems AA Howard, M Perego, GE Karniadakis, P Stinis Journal of Computational Physics 493, 112462, 2023 | 81* | 2023 |
| Learning unknown physics of non-Newtonian fluids B Reyes, AA Howard, P Perdikaris, AM Tartakovsky Physical Review Fluids 6 (7), 073301, 2021 | 67 | 2021 |
| A conservative level set method for N-phase flows with a free-energy-based surface tension model AA Howard, AM Tartakovsky Journal of Computational Physics 426, 109955, 2021 | 27 | 2021 |
| Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems AA Howard, B Jacob, SH Murphy, A Heinlein, P Stinis arXiv preprint arXiv:2406.19662, 2024 | 24 | 2024 |
| A hybrid deep neural operator/finite element method for ice-sheet modeling QZ He, M Perego, AA Howard, GE Karniadakis, P Stinis Journal of Computational Physics 492, 112428, 2023 | 19 | 2023 |
| Stacked networks improve physics-informed training: applications to neural networks and deep operator networks AA Howard, SH Murphy, SE Ahmed, P Stinis arXiv preprint arXiv:2311.06483, 2023 | 18 | 2023 |
| Machine Learning in Heterogeneous Porous Materials M D'Elia, H Deng, C Fraces, K Garikipati, L Graham-Brady, A Howard, ... arXiv preprint arXiv:2202.04137, 2022 | 14 | 2022 |
| Physics-informed CoKriging model of a redox flow battery AA Howard, T Yu, W Wang, AM Tartakovsky Journal of Power Sources 542, 231668, 2022 | 12 | 2022 |
| A multifidelity approach to continual learning for physical systems A Howard, Y Fu, P Stinis Machine Learning: Science and Technology 5 (2), 025042, 2024 | 8 | 2024 |
| Settling of heavy particles in concentrated suspensions of neutrally buoyant particles under uniform shear A Howard, M Maxey, K Yeo Fluid Dynamics Research, 2018 | 8 | 2018 |
| Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems A Heinlein, AA Howard, D Beecroft, P Stinis arXiv preprint arXiv:2401.07888, 2024 | 7 | 2024 |
| Simulation study of particle clouds in oscillating shear flow AA Howard, MR Maxey Journal of Fluid Mechanics 852, 484-506, 2018 | 7 | 2018 |
| Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks W Chen, AA Howard, P Stinis arXiv preprint arXiv:2407.01613, 2024 | 6 | 2024 |
| Dispersion of a suspension plug in oscillatory pressure-driven flow FR Cui, AA Howard, MR Maxey, A Tripathi Physical Review Fluids 2 (9), 094303, 2017 | 6 | 2017 |
| SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks B Jacob, AA Howard, P Stinis arXiv preprint arXiv:2411.06286, 2024 | 5 | 2024 |
| Efficient kernel surrogates for neural network-based regression S Qadeer, A Engel, A Howard, A Tsou, M Vargas, P Stinis, T Chiang arXiv preprint arXiv:2310.18612, 2023 | 5 | 2023 |
| Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance Y Fu, A Howard, C Zeng, Y Chen, P Gao, P Stinis ACS Energy Letters 9, 2767-2774, 2024 | 4 | 2024 |
| Machine learning methods for particle stress development in suspension Poiseuille flows AA Howard, J Dong, R Patel, M D’Elia, MR Maxey, P Stinis Rheologica Acta 62 (10), 507-534, 2023 | 4 | 2023 |
| Bidisperse suspension balance model AA Howard, MR Maxey, S Gallier Physical Review Fluids 7 (12), 124301, 2022 | 4 | 2022 |
| Non-local model for surface tension in fluid-fluid simulations AA Howard, AM Tartakovsky Journal of Computational Physics 421, 109732, 2020 | 4 | 2020 |
Alexandre TartakovskyUniversity of Illinois Urbana-Champaign and Pacific Northwest National LaboratoryVerified email at pnnl.gov
