Alexandre M. Tartakovsky
Orcid: 0000-0003-2375-318X
According to our database1,
Alexandre M. Tartakovsky
authored at least 63 papers
between 2007 and 2024.
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Bibliography
2024
Physics-informed machine learning method with space-time Karhunen-Loève expansions for forward and inverse partial differential equations.
J. Comput. Phys., February, 2024
Randomized physics-informed machine learning for uncertainty quantification in high-dimensional inverse problems.
J. Comput. Phys., 2024
Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems.
J. Comput. Phys., 2024
Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models.
CoRR, 2024
CoRR, 2024
2023
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling.
CoRR, 2023
2022
Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems.
J. Comput. Phys., 2022
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions.
CoRR, 2022
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery.
CoRR, 2022
Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems.
IEEE Access, 2022
2021
SIAM J. Sci. Comput., 2021
J. Comput. Phys., 2021
A conservative level set method for <i>N</i>-phase flows with a free-energy-based surface tension model.
J. Comput. Phys., 2021
CoRR, 2021
Physics-constrained deep neural network method for estimating parameters in a redox flow battery.
CoRR, 2021
CoRR, 2021
2020
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models.
J. Comput. Phys., 2020
Gaussian process regression and conditional polynomial chaos for parameter estimation.
J. Comput. Phys., 2020
J. Comput. Phys., 2020
An efficient epistemic uncertainty quantification algorithm for a class of stochastic models: A post-processing and domain decomposition framework.
CoRR, 2020
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids.
CoRR, 2020
CoRR, 2020
CoRR, 2020
CRNT4SBML: a Python package for the detection of bistability in biochemical reaction networks.
Bioinform., 2020
2019
Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence.
J. Comput. Phys., 2019
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks.
J. Comput. Phys., 2019
Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients.
J. Comput. Phys., 2019
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport.
CoRR, 2019
CoRR, 2019
CoRR, 2019
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations.
CoRR, 2019
Engineering structural robustness in power grid networks susceptible to community desynchronization.
Appl. Netw. Sci., 2019
Proceedings of the Third IEEE/ACM Workshop on Deep Learning on Supercomputers, 2019
Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids.
Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019
2018
Sliced-Inverse-Regression-Aided Rotated Compressive Sensing Method for Uncertainty Quantification.
SIAM/ASA J. Uncertain. Quantification, 2018
Stochastic Basis Adaptation and Spatial Domain Decomposition for Partial Differential Equations with Random Coefficients.
SIAM/ASA J. Uncertain. Quantification, 2018
Probability and Cumulative Density Function Methods for the Stochastic Advection-Reaction Equation.
SIAM/ASA J. Uncertain. Quantification, 2018
Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence.
CoRR, 2018
2017
Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients.
J. Comput. Phys., 2017
Modeling electrokinetic flows by consistent implicit incompressible smoothed particle hydrodynamics.
J. Comput. Phys., 2017
Solving differential equations with unknown constitutive relations as recurrent neural networks.
CoRR, 2017
2016
Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions.
Multiscale Model. Simul., 2016
Pairwise Force Smoothed Particle Hydrodynamics model for multiphase flow: Surface tension and contact line dynamics.
J. Comput. Phys., 2016
2015
Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input.
SIAM/ASA J. Uncertain. Quantification, 2015
SIAM/ASA J. Uncertain. Quantification, 2015
2014
SIAM J. Appl. Math., 2014
Smoothed particle hydrodynamics continuous boundary force method for Navier-Stokes equations subject to a Robin boundary condition.
J. Comput. Phys., 2014
2013
Multiscale Model. Simul., 2013
SIAM/ASA J. Uncertain. Quantification, 2013
J. Comput. Phys., 2013
Smoothed particle hydrodynamics non-Newtonian model for ice-sheet and ice-shelf dynamics.
J. Comput. Phys., 2013
2011
2010
Numerical Studies of Three-dimensional Stochastic Darcy's Equation and Stochastic Advection-Diffusion-Dispersion Equation.
J. Sci. Comput., 2010
Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids.
J. Comput. Phys., 2010
A Component-Based Framework for Smoothed Particle Hydrodynamics Simulations of Reactive Fluid Flow in Porous Media.
Int. J. High Perform. Comput. Appl., 2010
A novel method for modeling Neumann and Robin boundary conditions in smoothed particle hydrodynamics.
Comput. Phys. Commun., 2010
2009
2008
SIAM J. Sci. Comput., 2008
2007
Simulations of reactive transport and precipitation with smoothed particle hydrodynamics.
J. Comput. Phys., 2007