Alexandre M. Tartakovsky

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 50

Top Topics

Stochastic Simulation

Machine Learning

Data Assimilation

Differential Equations

Top Venues

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Publications

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 (2024)
Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky
Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation. CoRR (2024)
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 (2024)
Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems. CoRR (2023)
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 (2023)
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 (2023)
Marta D'Elia, Hang Deng, Cedric G. Fraces, Krishna 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 (2022)
Jing Li, Alexandre M. Tartakovsky
Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems. J. Comput. Phys. 462 (2022)
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 (2022)
QiZhi He, Yucheng Fu, Panos Stinis, Alexandre M. Tartakovsky
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery. CoRR (2022)
Yifei Zong, QiZhi He, Alexandre M. Tartakovsky
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions. CoRR (2022)
QiZhi He, Panos Stinis, Alexandre M. Tartakovsky
Physics-constrained deep neural network method for estimating parameters in a redox flow battery. CoRR (2021)
Amanda A. Howard, Alexandre M. Tartakovsky
Physics-informed CoKriging model of a redox flow battery. CoRR (2021)
Xiu Yang, Guzel Tartakovsky, Alexandre M. Tartakovsky
Physics Information Aided Kriging using Stochastic Simulation Models. SIAM J. Sci. Comput. 43 (6) (2021)
Amanda A. Howard, Alexandre M. Tartakovsky
-phase flows with a free-energy-based surface tension model. J. Comput. Phys. 426 (2021)
Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
Time-dependent stochastic basis adaptation for uncertainty quantification. CoRR (2021)
Alexandre M. Tartakovsky, David A. Barajas-Solano, QiZhi He
Physics-informed machine learning with conditional Karhunen-Loève expansions. J. Comput. Phys. 426 (2021)
Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems. CoRR (2021)
Kailai Xu, Alexandre M. Tartakovsky, Jeff Burghardt, Eric Darve
Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning. CoRR (2020)
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 (2020)
Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky
Learning Unknown Physics of non-Newtonian Fluids. CoRR (2020)
Peiyuan Gao, Xiu Yang, Alexandre M. Tartakovsky
Learning Coarse-Grained Potentials for Binary Fluids. J. Chem. Inf. Model. 60 (8) (2020)
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 (2020)
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) (2020)
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 (2020)
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 (2020)
Jing Li, Alexandre M. Tartakovsky
Gaussian process regression and conditional polynomial chaos for parameter estimation. J. Comput. Phys. 416 (2020)
Amanda A. Howard, Alexandre M. Tartakovsky
Non-local model for surface tension in fluid-fluid simulations. J. Comput. Phys. 421 (2020)
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)
Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. J. Comput. Phys. 397 (2019)
David A. Barajas-Solano, Alexandre M. Tartakovsky
Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients. J. Comput. Phys. 395 (2019)
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 (2019)
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)
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 (2019)
QiZhi He, David Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport. CoRR (2019)
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) (2019)
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 (2019)
Jing Li, Alexandre M. Tartakovsky
Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation. CoRR (2019)
Tong Ma, Renke Huang, David A. Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression. CoRR (2019)
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) (2018)
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 (2018)
Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. CoRR (2018)
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) (2018)
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) (2018)
Xiu Yang, Guzel Tartakovsky, Alexandre M. Tartakovsky
Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence. CoRR (2018)
Tobias Hagge, Panos Stinis, Enoch Yeung, Alexandre M. Tartakovsky
Solving differential equations with unknown constitutive relations as recurrent neural networks. CoRR (2017)
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 (2017)
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 (2017)
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) (2016)
Alexandre M. Tartakovsky, Alexander Panchenko
Pairwise Force Smoothed Particle Hydrodynamics model for multiphase flow: Surface tension and contact line dynamics. J. Comput. Phys. 305 (2016)
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) (2015)
K. D. Jarman Jr., Alexandre M. Tartakovsky
Erratum: A Comparison of Closures for Stochastic Advection-Diffusion Equations. SIAM/ASA J. Uncertain. Quantification 3 (1) (2015)
Alexander Panchenko, Alexandre M. Tartakovsky, Kevin Cooper
Discrete Models of Fluids: Spatial Averaging, Closure, and Model Reduction. SIAM J. Appl. Math. 74 (2) (2014)
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 (2014)
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 (2013)
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 (2013)
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) (2013)
K. D. Jarman Jr., Alexandre M. Tartakovsky
A Comparison of Closures for Stochastic Advection-Diffusion Equations. SIAM/ASA J. Uncertain. Quantification 1 (1) (2013)
Alexandre M. Tartakovsky, Alexander Panchenko, Kim F. Ferris
Dimension reduction method for ODE fluid models. J. Comput. Phys. 230 (23) (2011)
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) (2010)
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) (2010)
Emily M. Ryan, Alexandre M. Tartakovsky, Cristina H. Amon
A novel method for modeling Neumann and Robin boundary conditions in smoothed particle hydrodynamics. Comput. Phys. Commun. 181 (12) (2010)
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) (2010)
Alexandre M. Tartakovsky, Kim F. Ferris, Paul Meakin
Lagrangian particle model for multiphase flows. Comput. Phys. Commun. 180 (10) (2009)
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) (2008)
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) (2007)