| Physics‐informed deep neural networks for learning parameters and constitutive relationships in subsurface flow problems AM Tartakovsky, CO Marrero, P Perdikaris, GD Tartakovsky, ... Water Resources Research 56 (5), e2019WR026731, 2020 | 375 | 2020 |
| Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport QZ He, D Barajas-Solano, G Tartakovsky, AM Tartakovsky Advances in Water Resources 141, 103610, 2020 | 312 | 2020 |
| Learning parameters and constitutive relationships with physics informed deep neural networks AM Tartakovsky, CO Marrero, P Perdikaris, GD Tartakovsky, ... arXiv preprint arXiv:1808.03398, 2018 | 145 | 2018 |
| Sparsifying priors for Bayesian uncertainty quantification in model discovery SM Hirsh, DA Barajas-Solano, JN Kutz Royal Society Open Science 9 (2), 211823, 2022 | 93 | 2022 |
| Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence X Yang, D Barajas-Solano, G Tartakovsky, AM Tartakovsky Journal of Computational Physics 395, 410-431, 2019 | 85 | 2019 |
| Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs L Yang, S Treichler, T Kurth, K Fischer, D Barajas-Solano, J Romero, ... 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS), 1-11, 2019 | 60 | 2019 |
| Physics-informed machine learning with conditional Karhunen-Loève expansions AM Tartakovsky, DA Barajas-Solano, Q He Journal of Computational Physics 426, 109904, 2021 | 32 | 2021 |
| Stochastic collocation methods for nonlinear parabolic equations with random coefficients DA Barajas-Solano, DM Tartakovsky SIAM/ASA Journal on Uncertainty Quantification 4 (1), 475-494, 2016 | 32 | 2016 |
| Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models R Tipireddy, DA Barajas-Solano, AM Tartakovsky Journal of Computational Physics 418, 109604, 2020 | 24 | 2020 |
| Physics‐informed machine learning method for large‐scale data assimilation problems YH Yeung, DA Barajas‐Solano, AM Tartakovsky Water Resources Research 58 (5), e2021WR031023, 2022 | 22 | 2022 |
| Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients DA Barajas-Solano, AM Tartakovsky Journal of Computational Physics 395, 247-262, 2019 | 20 | 2019 |
| Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems D Dylewsky, D Barajas-Solano, T Ma, AM Tartakovsky, JN Kutz IEEE Access 10, 33440-33448, 2022 | 19 | 2022 |
| Probabilistic density function method for stochastic ODEs of power systems with uncertain power input P Wang, DA Barajas-Solano, E Constantinescu, S Abhyankar, D Ghosh, ... SIAM/ASA Journal on Uncertainty Quantification 3 (1), 873-896, 2015 | 19 | 2015 |
| Probabilistic density function method for nonlinear dynamical systems driven by colored noise DA Barajas-Solano, AM Tartakovsky Physical Review E 93 (5), 052121, 2016 | 18 | 2016 |
| Physics-informed Gaussian process regression for states estimation and forecasting in power grids AM Tartakovsky, T Ma, DA Barajas-Solano, R Tipireddy International Journal of Forecasting 39 (2), 967-980, 2023 | 17 | 2023 |
| Linear functional minimization for inverse modeling DA Barajas‐Solano, BE Wohlberg, VV Vesselinov, DM Tartakovsky Water Resources Research 51 (6), 4516-4531, 2015 | 15 | 2015 |
| Efficient gHMC reconstruction of contaminant release history DA Barajas-Solano, FJ Alexander, M Anghel, DM Tartakovsky Frontiers in Environmental Science 7, 149, 2019 | 10 | 2019 |
| Electric load and power forecasting using ensemble gaussian process regression T Ma, DA Barajas-Solano, R Huang, AM Tartakovsky Journal of Machine Learning for Modeling and Computing 3 (2), 2022 | 9 | 2022 |
| Dynamic mode decomposition for forecasting and analysis of power grid load data D Dylewsky, D Barajas-Solano, T Ma, AM Tartakovsky, JN Kutz arXiv preprint arXiv:2010.04248, 2020 | 9 | 2020 |
| A kinetic Monte Carlo approach for simulating cascading transmission line failure J Roth, DA Barajas-Solano, P Stinis, J Weare, M Anitescu arXiv preprint arXiv:1912.08081, 2019 | 9 | 2019 |
