| Problem reduction, renormalization, and memory A Chorin, P Stinis Communications in Applied Mathematics and Computational Science 1 (1), 1-27, 2007 | 135 | 2007 |
| Improved particle filters for multi-target tracking V Maroulas, P Stinis Journal of Computational Physics 231 (2), 602-611, 2012 | 66 | 2012 |
| Renormalized Mori–Zwanzig-reduced models for systems without scale separation P Stinis Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015 | 58 | 2015 |
| Optimal prediction and the rate of decay for solutions of the Euler equations in two and three dimensions OH Hald, P Stinis Proceedings of the National Academy of Sciences 104 (16), 6527-6532, 2007 | 52 | 2007 |
| Higher order Mori–Zwanzig models for the Euler equations P Stinis Multiscale Modeling & Simulation 6 (3), 741-760, 2007 | 49 | 2007 |
| Multistep and continuous physics-informed neural network methods for learning governing equations and constitutive relations R Tipireddy, P Perdikaris, P Stinis, AM Tartakovsky Journal of Machine Learning for Modeling and Computing 3 (2), 2022 | 45* | 2022 |
| 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 | 42* | 2023 |
| Enforcing constraints for interpolation and extrapolation in generative adversarial networks P Stinis, T Hagge, AM Tartakovsky, E Yeung Journal of Computational Physics 397, 108844, 2019 | 37 | 2019 |
| Machine learning structure preserving brackets for forecasting irreversible processes K Lee, N Trask, P Stinis Advances in Neural Information Processing Systems 34, 5696-5707, 2021 | 32 | 2021 |
| Stochastic optimal prediction for the Kuramoto--Sivashinsky equation P Stinis Multiscale Modeling & Simulation 2 (4), 580-612, 2004 | 30 | 2004 |
| A comparative study of two stochastic mode reduction methods P Stinis Physica D: Nonlinear Phenomena 213 (2), 197-213, 2006 | 28 | 2006 |
| Renormalized reduced models for singular PDEs P Stinis Communications in Applied Mathematics and Computational Science 8 (1), 39-66, 2013 | 27 | 2013 |
| Variance reduction for particle filters of systems with time scale separation D Givon, P Stinis, J Weare IEEE Transactions on Signal Processing 57 (2), 424-435, 2008 | 26 | 2008 |
| Physics-constrained deep neural network method for estimating parameters in a redox flow battery QZ He, P Stinis, AM Tartakovsky Journal of Power Sources 528, 231147, 2022 | 25 | 2022 |
| Doing the impossible: Why neural networks can be trained at all NO Hodas, P Stinis Frontiers in psychology 9, 292329, 2018 | 25 | 2018 |
| Solving differential equations with unknown constitutive relations as recurrent neural networks T Hagge, P Stinis, E Yeung, AM Tartakovsky arXiv preprint arXiv:1710.02242, 2017 | 25 | 2017 |
| A maximum likelihood algorithm for the estimation and renormalization of exponential densities P Stinis Journal of Computational Physics 208 (2), 691-703, 2005 | 23 | 2005 |
| Machine-learning-based spectral methods for partial differential equations B Meuris, S Qadeer, P Stinis Scientific Reports 13 (1), 1739, 2023 | 20* | 2023 |
| Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations W Chen, P Stinis arXiv preprint arXiv:2303.11577, 2023 | 18 | 2023 |
| Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning P Stinis arXiv preprint arXiv:1905.07501, 2019 | 17 | 2019 |
