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Alexandre M. Tartakovsky
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2020 – today
- 2024
- [j40]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) - [j39]Yu-Hong Yeung, David A. Barajas-Solano
Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems. J. Comput. Phys. 502: 112788 (2024) - [j38]Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky
Randomized physics-informed machine learning for uncertainty quantification in high-dimensional inverse problems. J. Comput. Phys. 519: 113395 (2024) - [i26]Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky:
Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation. CoRR abs/2407.04617 (2024) - [i25]Yuanzhe Wang, Alexandre M. Tartakovsky:
Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models. CoRR abs/2408.11145 (2024) - 2023
- [i24]Yu-Hong Yeung
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
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems. CoRR abs/2312.06177 (2023) - 2022
- [j37]Daniel Dylewsky
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
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
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions. CoRR abs/2208.08635 (2022) - 2021
- [j35]Alexandre M. Tartakovsky
Physics-informed machine learning with conditional Karhunen-Loève expansions. J. Comput. Phys. 426: 109904 (2021) - [j34]Amanda A. Howard
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
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
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
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
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)
2010 – 2019
- 2019
- [j27]Daniel Dylewsky
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
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
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
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
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
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
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
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
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
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
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
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
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
- [j7]Emily M. Ryan
A novel method for modeling Neumann and Robin boundary conditions in smoothed particle hydrodynamics. Comput. Phys. Commun. 181(12): 2008-2023 (2010) - [j6]Bruce J. Palmer
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)
2000 – 2009
- 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
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
Simulations of reactive transport and precipitation with smoothed particle hydrodynamics. J. Comput. Phys. 222(2): 654-672 (2007)
Coauthor Index
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