Alexandre M. Tartakovsky

74 papers Journal 72Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Chance constrained load frequency control of power systems with wind resources. J. Frankl. Inst. Tong Ma, David Alonso Barajas-Solano, Alexandre M. Tartakovsky
2025	J	`jnl`	Mathematics of Digital Twins and Transfer Learning for PDE Models. CoRR Yifei Zong, Alexandre M. Tartakovsky
2025	J	`jnl`	Parameter Estimation in River Transport Models With Immobile Phase Exchange Using Dimensional Analysis and Reduced-Order Models. CoRR Manuel M. Reyna, Alexandre M. Tartakovsky
2025	J	`jnl`	Total uncertainty quantification in inverse solutions with deep learning surrogate models. J. Comput. Phys. Yuanzhe Wang, James L. McCreight, Joseph D. Hughes, Alexandre M. Tartakovsky
2025	J	`jnl`	VAE-DNN: Energy-Efficient Trainable-by-Parts Surrogate Model For Parametric Partial Differential Equations. CoRR Yifei Zong, Alexandre M. Tartakovsky
2024	J	`jnl`	Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems. J. Comput. Phys. Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
2024	J	`jnl`	Physics-informed machine learning method with space-time Karhunen-Loève expansions for forward and inverse partial differential equations. J. Comput. Phys. Alexandre M. Tartakovsky, Yifei Zong
2024	J	`jnl`	Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation. CoRR Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky
2024	J	`jnl`	Randomized physics-informed machine learning for uncertainty quantification in high-dimensional inverse problems. J. Comput. Phys. Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky
2024	J	`jnl`	Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data. CoRR Jice Zeng, Yuanzhe Wang, Alexandre M. Tartakovsky, David Barajas-Solano
2024	J	`jnl`	Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models. CoRR Yuanzhe Wang, Alexandre M. Tartakovsky
2023	J	`jnl`	Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling. CoRR Yu-Hong Yeung, Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky
2023	J	`jnl`	Gaussian process regression and conditional Karhunen-Loéve models for data assimilation in inverse problems. CoRR Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
2023	J	`jnl`	Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems. CoRR Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky
2022	J	`jnl`	Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery. CoRR Qizhi He, Yucheng Fu, Panos Stinis, Alexandre M. Tartakovsky
2022	J	`jnl`	Machine Learning in Heterogeneous Porous Materials. CoRR 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
2022	J	`jnl`	Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions. CoRR Yifei Zong, Qizhi He, Alexandre M. Tartakovsky
2022	J	`jnl`	Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems. J. Comput. Phys. Jing Li, Alexandre M. Tartakovsky
2022	J	`jnl`	Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems. IEEE Access Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz
2021	J	`jnl`	A conservative level set method for J. Comput. Phys. Amanda A. Howard, Alexandre M. Tartakovsky
2021	J	`jnl`	Physics Information Aided Kriging using Stochastic Simulation Models. SIAM J. Sci. Comput. Xiu Yang, Guzel Tartakovsky, Alexandre M. Tartakovsky
2021	J	`jnl`	Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems. CoRR Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
2021	J	`jnl`	Physics-constrained deep neural network method for estimating parameters in a redox flow battery. CoRR Qizhi He, Panos Stinis, Alexandre M. Tartakovsky
2021	J	`jnl`	Physics-informed CoKriging model of a redox flow battery. CoRR Amanda A. Howard, Alexandre M. Tartakovsky
2021	J	`jnl`	Physics-informed machine learning with conditional Karhunen-Loève expansions. J. Comput. Phys. Alexandre M. Tartakovsky, David A. Barajas-Solano, Qizhi He
2021	J	`jnl`	Time-dependent stochastic basis adaptation for uncertainty quantification. CoRR Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
2020	J	`jnl`	An efficient epistemic uncertainty quantification algorithm for a class of stochastic models: A post-processing and domain decomposition framework. CoRR Mahadevan Ganesh, Stuart C. Hawkins, Alexandre M. Tartakovsky, Ramakrishna Tipireddy
2020	J	`jnl`	CRNT4SBML: a Python package for the detection of bistability in biochemical reaction networks. Bioinform. Brandon Reyes, Irene Otero-Muras, Michael T. Shuen, Alexandre M. Tartakovsky, Vladislav A. Petyuk
2020	J	`jnl`	Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models. J. Comput. Phys. Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky
2020	J	`jnl`	Dynamic mode decomposition for forecasting and analysis of power grid load data. CoRR Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz
2020	J	`jnl`	Gaussian process regression and conditional polynomial chaos for parameter estimation. J. Comput. Phys. Jing Li, Alexandre M. Tartakovsky
2020	J	`jnl`	Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning. CoRR Kailai Xu, Alexandre M. Tartakovsky, Jeff Burghardt, Eric Darve
2020	J	`jnl`	Learning Coarse-Grained Potentials for Binary Fluids. J. Chem. Inf. Model. Peiyuan Gao, Xiu Yang, Alexandre M. Tartakovsky
2020	J	`jnl`	Learning Unknown Physics of non-Newtonian Fluids. CoRR Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky
2020	J	`jnl`	Non-local model for surface tension in fluid-fluid simulations. J. Comput. Phys. Amanda A. Howard, Alexandre M. Tartakovsky
2020	J	`jnl`	Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids. CoRR Tong Ma, David Alonso Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky
2019	J	`jnl`	A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations. CoRR Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky
2019	J	`jnl`	Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients. J. Comput. Phys. David A. Barajas-Solano, Alexandre M. Tartakovsky
2019	J	`jnl`	Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression. CoRR Tong Ma, Renke Huang, David A. Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky
2019	J	`jnl`	Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. J. Comput. Phys. Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung
2019	J	`jnl`	Engineering structural robustness in power grid networks susceptible to community desynchronization. Appl. Netw. Sci. Daniel Dylewsky, Xiu Yang, Alexandre M. Tartakovsky, J. Nathan Kutz
2019	J	`jnl`	Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation. CoRR Jing Li, Alexandre M. Tartakovsky
2019	—	`conf`	Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. DLS@SC 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
2019	J	`jnl`	Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. CoRR 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
2019	J	`jnl`	Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport. CoRR Qizhi He, David Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky
2019	J	`jnl`	Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence. J. Comput. Phys. Xiu Yang, David A. Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky
2019	—	`conf`	Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids. HICSS Alexandre M. Tartakovsky, Ramakrishna Tipireddy
2018	J	`jnl`	Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. CoRR Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung
2018	J	`jnl`	Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence. CoRR Xiu Yang, David A. Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky
2018	J	`jnl`	Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence. CoRR Xiu Yang, Guzel Tartakovsky, Alexandre M. Tartakovsky
2018	J	`jnl`	Probability and Cumulative Density Function Methods for the Stochastic Advection-Reaction Equation. SIAM/ASA J. Uncertain. Quantification David A. Barajas-Solano, Alexandre M. Tartakovsky
2018	J	`jnl`	Sliced-Inverse-Regression-Aided Rotated Compressive Sensing Method for Uncertainty Quantification. SIAM/ASA J. Uncertain. Quantification Xiu Yang, Weixuan Li, Alexandre M. Tartakovsky
2018	J	`jnl`	Stochastic Basis Adaptation and Spatial Domain Decomposition for Partial Differential Equations with Random Coefficients. SIAM/ASA J. Uncertain. Quantification Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
2017	J	`jnl`	Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients. J. Comput. Phys. Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
2017	J	`jnl`	Modeling electrokinetic flows by consistent implicit incompressible smoothed particle hydrodynamics. J. Comput. Phys. Wenxiao Pan, Kyungjoo Kim, Mauro Perego, Alexandre M. Tartakovsky, Michael L. Parks
2017	J	`jnl`	Solving differential equations with unknown constitutive relations as recurrent neural networks. CoRR Tobias Hagge, Panos Stinis, Enoch Yeung, Alexandre M. Tartakovsky
2016	J	`jnl`	Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions. Multiscale Model. Simul. David A. Barajas-Solano, Alexandre M. Tartakovsky
2016	J	`jnl`	Pairwise Force Smoothed Particle Hydrodynamics model for multiphase flow: Surface tension and contact line dynamics. J. Comput. Phys. Alexandre M. Tartakovsky, Alexander Panchenko
2015	J	`jnl`	Erratum: A Comparison of Closures for Stochastic Advection-Diffusion Equations. SIAM/ASA J. Uncertain. Quantification K. D. Jarman Jr., Alexandre M. Tartakovsky
2015	J	`jnl`	Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input. SIAM/ASA J. Uncertain. Quantification Peng Wang, David A. Barajas-Solano, Emil M. Constantinescu, Shrirang Abhyankar, Debojyoti Ghosh, Barry F. Smith, Zhenyu Huang, Alexandre M. Tartakovsky
2014	J	`jnl`	Discrete Models of Fluids: Spatial Averaging, Closure, and Model Reduction. SIAM J. Appl. Math. Alexander Panchenko, Alexandre M. Tartakovsky, Kevin Cooper
2014	J	`jnl`	Smoothed particle hydrodynamics continuous boundary force method for Navier-Stokes equations subject to a Robin boundary condition. J. Comput. Phys. Wenxiao Pan, Jie Bao, Alexandre M. Tartakovsky
2013	J	`jnl`	A Comparison of Closures for Stochastic Advection-Diffusion Equations. SIAM/ASA J. Uncertain. Quantification K. D. Jarman Jr., Alexandre M. Tartakovsky
2013	J	`jnl`	CDF Solutions of Buckley-Leverett Equation with Uncertain Parameters. Multiscale Model. Simul. Peng Wang, Daniel M. Tartakovsky, K. D. Jarman Jr., Alexandre M. Tartakovsky
2013	J	`jnl`	Exact PDF equations and closure approximations for advective-reactive transport. J. Comput. Phys. Daniele Venturi, Daniel M. Tartakovsky, Alexandre M. Tartakovsky, George E. Karniadakis
2013	J	`jnl`	Smoothed particle hydrodynamics non-Newtonian model for ice-sheet and ice-shelf dynamics. J. Comput. Phys. Wenxiao Pan, Alexandre M. Tartakovsky, Joe J. Monaghan
2011	J	`jnl`	Dimension reduction method for ODE fluid models. J. Comput. Phys. Alexandre M. Tartakovsky, Alexander Panchenko, Kim F. Ferris
2010	J	`jnl`	A Component-Based Framework for Smoothed Particle Hydrodynamics Simulations of Reactive Fluid Flow in Porous Media. Int. J. High Perform. Comput. Appl. Bruce J. Palmer, Vidhya Gurumoorthi, Alexandre M. Tartakovsky, Timothy D. Scheibe
2010	J	`jnl`	A novel method for modeling Neumann and Robin boundary conditions in smoothed particle hydrodynamics. Comput. Phys. Commun. Emily M. Ryan, Alexandre M. Tartakovsky, Cristina H. Amon
2010	J	`jnl`	Numerical Studies of Three-dimensional Stochastic Darcy's Equation and Stochastic Advection-Diffusion-Dispersion Equation. J. Sci. Comput. Guang Lin, Alexandre M. Tartakovsky
2010	J	`jnl`	Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids. J. Comput. Phys. Guang Lin, Alexandre M. Tartakovsky, Daniel M. Tartakovsky
2009	J	`jnl`	Lagrangian particle model for multiphase flows. Comput. Phys. Commun. Alexandre M. Tartakovsky, Kim F. Ferris, Paul Meakin
2008	J	`jnl`	Hybrid Simulations of Reaction-Diffusion Systems in Porous Media. SIAM J. Sci. Comput. Alexandre M. Tartakovsky, Daniel M. Tartakovsky, Timothy D. Scheibe, Paul Meakin
2007	J	`jnl`	Simulations of reactive transport and precipitation with smoothed particle hydrodynamics. J. Comput. Phys. Alexandre M. Tartakovsky, Paul Meakin, Timothy D. Scheibe, Rogene M. Eichler West