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ORCIDAlexandre M. Tartakovsky
Publications
- 2024
[j39]Alexandre M. Tartakovsky
, Yifei Zong:
Physics-informed machine learning method with space-time Karhunen-Loève expansions for forward and inverse partial differential equations. J. Comput. Phys. 499: 112723 (2024)- [j38]Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky:
Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems. J. Comput. Phys. 502: 112788 (2024) - [i26]Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky:
Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation. CoRR abs/2407.04617 (2024) - 2023
- [i24]Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky:
Gaussian process regression and conditional Karhunen-Loéve models for data assimilation in inverse problems. CoRR abs/2301.11279 (2023) - [i23]Yu-Hong Yeung, Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky:
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling. CoRR abs/2307.02572 (2023) - [i22]Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky:
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems. CoRR abs/2312.06177 (2023) - 2022
- [j37]Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz:
Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems. IEEE Access 10: 33440-33448 (2022) - [j36]Jing Li, Alexandre M. Tartakovsky:
Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems. J. Comput. Phys. 462: 111230 (2022) - [i21]Marta D'Elia, Hang Deng, Cedric G. Fraces, Krishna C. Garikipati, Lori Graham-Brady, Amanda A. Howard, George Em Karniadakis, Vahid Keshavarzzadeh, Robert M. Kirby, J. Nathan Kutz, Chunhui Li, Xing Liu, Hannah Lu, Pania Newell, Daniel O'Malley, Masa Prodanovic, Gowri Srinivasan, Alexandre M. Tartakovsky, Daniel M. Tartakovsky, Hamdi A. Tchelepi, Bozo Vazic, Hari S. Viswanathan, Hongkyu Yoon, Piotr Zarzycki:
Machine Learning in Heterogeneous Porous Materials. CoRR abs/2202.04137 (2022) - [i20]QiZhi He, Yucheng Fu, Panos Stinis, Alexandre M. Tartakovsky:
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery. CoRR abs/2203.01985 (2022) - [i19]Yifei Zong, QiZhi He, Alexandre M. Tartakovsky:
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions. CoRR abs/2208.08635 (2022) - 2021
- [j35]Alexandre M. Tartakovsky, David A. Barajas-Solano, QiZhi He:
Physics-informed machine learning with conditional Karhunen-Loève expansions. J. Comput. Phys. 426: 109904 (2021) - [j34]Amanda A. Howard, Alexandre M. Tartakovsky:
A conservative level set method for N-phase flows with a free-energy-based surface tension model. J. Comput. Phys. 426: 109955 (2021) - [j33]Xiu Yang, Guzel Tartakovsky, Alexandre M. Tartakovsky:
Physics Information Aided Kriging using Stochastic Simulation Models. SIAM J. Sci. Comput. 43(6): A3862-A3891 (2021) - [i18]Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky:
Time-dependent stochastic basis adaptation for uncertainty quantification. CoRR abs/2103.03316 (2021) - [i17]Amanda A. Howard, Alexandre M. Tartakovsky:
Physics-informed CoKriging model of a redox flow battery. CoRR abs/2106.09188 (2021) - [i16]QiZhi He, Panos Stinis, Alexandre M. Tartakovsky:
Physics-constrained deep neural network method for estimating parameters in a redox flow battery. CoRR abs/2106.11451 (2021) - [i15]Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky:
Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems. CoRR abs/2108.00037 (2021) - 2020
- [j32]Brandon Reyes, Irene Otero-Muras, Michael T. Shuen, Alexandre M. Tartakovsky, Vladislav A. Petyuk:
CRNT4SBML: a Python package for the detection of bistability in biochemical reaction networks. Bioinform. 36(12): 3922-3924 (2020) - [j31]Peiyuan Gao, Xiu Yang, Alexandre M. Tartakovsky:
Learning Coarse-Grained Potentials for Binary Fluids. J. Chem. Inf. Model. 60(8): 3731-3745 (2020) - [j30]Jing Li, Alexandre M. Tartakovsky:
Gaussian process regression and conditional polynomial chaos for parameter estimation. J. Comput. Phys. 416: 109520 (2020) - [j29]Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky:
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models. J. Comput. Phys. 418: 109604 (2020) - [j28]Amanda A. Howard, Alexandre M. Tartakovsky:
Non-local model for surface tension in fluid-fluid simulations. J. Comput. Phys. 421: 109732 (2020) - [i14]Kailai Xu, Alexandre M. Tartakovsky, Jeff Burghardt, Eric Darve:
Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning. CoRR abs/2005.04384 (2020) - [i13]Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky:
Learning Unknown Physics of non-Newtonian Fluids. CoRR abs/2009.01658 (2020) - [i12]Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz:
Dynamic mode decomposition for forecasting and analysis of power grid load data. CoRR abs/2010.04248 (2020) - [i11]Tong Ma, David Alonso Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky:
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids. CoRR abs/2010.04591 (2020) - [i10]Mahadevan Ganesh, Stuart C. Hawkins, Alexandre M. Tartakovsky, Ramakrishna Tipireddy:
An efficient epistemic uncertainty quantification algorithm for a class of stochastic models: A post-processing and domain decomposition framework. CoRR abs/2010.07863 (2020) - 2019
- [j27]Daniel Dylewsky, Xiu Yang, Alexandre M. Tartakovsky, J. Nathan Kutz:
Engineering structural robustness in power grid networks susceptible to community desynchronization. Appl. Netw. Sci. 4(1): 24:1-24:14 (2019) - [j26]David A. Barajas-Solano, Alexandre M. Tartakovsky:
Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients. J. Comput. Phys. 395: 247-262 (2019) - [j25]Xiu Yang, David A. Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky:
Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence. J. Comput. Phys. 395: 410-431 (2019) - [j24]Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung:
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. J. Comput. Phys. 397 (2019) - [c2]Alexandre M. Tartakovsky, Ramakrishna Tipireddy:
Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids. HICSS 2019: 1-7 - [c1]Liu Yang, Prabhat, George E. Karniadakis, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston:
Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. DLS@SC 2019: 1-11 - [i9]Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky:
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations. CoRR abs/1904.04058 (2019) - [i8]Jing Li, Alexandre M. Tartakovsky:
Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation. CoRR abs/1908.00424 (2019) - [i7]Tong Ma, Renke Huang, David A. Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky:
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression. CoRR abs/1910.03783 (2019) - [i6]Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston, Prabhat, George E. Karniadakis:
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. CoRR abs/1910.13444 (2019) - [i5]QiZhi He, David Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky:
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport. CoRR abs/1912.02968 (2019) - 2018
- [j23]David A. Barajas-Solano, Alexandre M. Tartakovsky:
Probability and Cumulative Density Function Methods for the Stochastic Advection-Reaction Equation. SIAM/ASA J. Uncertain. Quantification 6(1): 180-212 (2018) - [j22]Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky:
Stochastic Basis Adaptation and Spatial Domain Decomposition for Partial Differential Equations with Random Coefficients. SIAM/ASA J. Uncertain. Quantification 6(1): 273-301 (2018) - [j21]Xiu Yang, Weixuan Li, Alexandre M. Tartakovsky:
Sliced-Inverse-Regression-Aided Rotated Compressive Sensing Method for Uncertainty Quantification. SIAM/ASA J. Uncertain. Quantification 6(4): 1532-1554 (2018) - [i4]Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung:
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. CoRR abs/1803.08182 (2018) - [i3]Xiu Yang, Guzel Tartakovsky, Alexandre M. Tartakovsky:
Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence. CoRR abs/1809.03461 (2018) - [i2]Xiu Yang, David A. Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky:
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence. CoRR abs/1811.09757 (2018) - 2017
- [j20]Wenxiao Pan, Kyungjoo Kim, Mauro Perego, Alexandre M. Tartakovsky, Michael L. Parks:
Modeling electrokinetic flows by consistent implicit incompressible smoothed particle hydrodynamics. J. Comput. Phys. 334: 125-144 (2017) - [j19]Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky:
Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients. J. Comput. Phys. 351: 203-215 (2017) - [i1]Tobias Hagge, Panos Stinis, Enoch Yeung, Alexandre M. Tartakovsky:
Solving differential equations with unknown constitutive relations as recurrent neural networks. CoRR abs/1710.02242 (2017) - 2016
- [j18]Alexandre M. Tartakovsky, Alexander Panchenko:
Pairwise Force Smoothed Particle Hydrodynamics model for multiphase flow: Surface tension and contact line dynamics. J. Comput. Phys. 305: 1119-1146 (2016) - [j17]David A. Barajas-Solano, Alexandre M. Tartakovsky:
Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions. Multiscale Model. Simul. 14(4): 1341-1376 (2016) - 2015
- [j16]K. D. Jarman Jr., Alexandre M. Tartakovsky:
Erratum: A Comparison of Closures for Stochastic Advection-Diffusion Equations. SIAM/ASA J. Uncertain. Quantification 3(1): 265-266 (2015) - [j15]Peng Wang, David A. Barajas-Solano, Emil M. Constantinescu, Shrirang Abhyankar, Debojyoti Ghosh, Barry F. Smith, Zhenyu Huang, Alexandre M. Tartakovsky:
Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input. SIAM/ASA J. Uncertain. Quantification 3(1): 873-896 (2015) - 2014
- [j14]Wenxiao Pan, Jie Bao, Alexandre M. Tartakovsky:
Smoothed particle hydrodynamics continuous boundary force method for Navier-Stokes equations subject to a Robin boundary condition. J. Comput. Phys. 259: 242-259 (2014) - [j13]Alexander Panchenko, Alexandre M. Tartakovsky, Kevin Cooper:
Discrete Models of Fluids: Spatial Averaging, Closure, and Model Reduction. SIAM J. Appl. Math. 74(2): 477-515 (2014) - 2013
- [j12]Wenxiao Pan, Alexandre M. Tartakovsky, Joe J. Monaghan:
Smoothed particle hydrodynamics non-Newtonian model for ice-sheet and ice-shelf dynamics. J. Comput. Phys. 242: 828-842 (2013) - [j11]Daniele Venturi, Daniel M. Tartakovsky, Alexandre M. Tartakovsky, George E. Karniadakis:
Exact PDF equations and closure approximations for advective-reactive transport. J. Comput. Phys. 243: 323-343 (2013) - [j10]K. D. Jarman Jr., Alexandre M. Tartakovsky:
A Comparison of Closures for Stochastic Advection-Diffusion Equations. SIAM/ASA J. Uncertain. Quantification 1(1): 319-347 (2013) - [j9]Peng Wang, Daniel M. Tartakovsky, K. D. Jarman Jr., Alexandre M. Tartakovsky:
CDF Solutions of Buckley-Leverett Equation with Uncertain Parameters. Multiscale Model. Simul. 11(1): 118-133 (2013) - 2011
- [j8]Alexandre M. Tartakovsky, Alexander Panchenko, Kim F. Ferris:
Dimension reduction method for ODE fluid models. J. Comput. Phys. 230(23): 8554-8572 (2011) - 2010
- [j6]Bruce J. Palmer, Vidhya Gurumoorthi, Alexandre M. Tartakovsky, Timothy D. Scheibe:
A Component-Based Framework for Smoothed Particle Hydrodynamics Simulations of Reactive Fluid Flow in Porous Media. Int. J. High Perform. Comput. Appl. 24(2): 228-239 (2010) - [j5]G. Lin, Alexandre M. Tartakovsky, Daniel M. Tartakovsky:
Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids. J. Comput. Phys. 229(19): 6995-7012 (2010) - [j4]G. Lin, Alexandre M. Tartakovsky:
Numerical Studies of Three-dimensional Stochastic Darcy's Equation and Stochastic Advection-Diffusion-Dispersion Equation. J. Sci. Comput. 43(1): 92-117 (2010) - 2009
- [j3]Alexandre M. Tartakovsky, Kim F. Ferris, Paul Meakin:
Lagrangian particle model for multiphase flows. Comput. Phys. Commun. 180(10): 1874-1881 (2009) - 2008
- [j2]Alexandre M. Tartakovsky, Daniel M. Tartakovsky, Timothy D. Scheibe, Paul Meakin:
Hybrid Simulations of Reaction-Diffusion Systems in Porous Media. SIAM J. Sci. Comput. 30(6): 2799-2816 (2008) - 2007
- [j1]Alexandre M. Tartakovsky, Paul Meakin, Timothy D. Scheibe, Rogene M. Eichler West:
Simulations of reactive transport and precipitation with smoothed particle hydrodynamics. J. Comput. Phys. 222(2): 654-672 (2007)
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