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A. Tartakovsky · SciRah

Researcher profile · Semantic Scholar

A. Tartakovsky

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A. Tartakovsky — researcher profile on SciRah

h-index: 45
Works: 264
Citations: 8,185

Most-cited papers

Differentiable modelling to unify machine learning and physical models for geosciences
Chao-Peng Shen, A. Appling, P. Gentine, T. Bandai +27 more
2023Nature Reviews Earth & Environment448 citations Open access
Semantic Scholar
Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems
A. Tartakovsky, Carlos Ortiz Marrero, P. Perdikaris, G. Tartakovsky +1 more
2020Water Resources Research434 citations
Semantic Scholar
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
Qi-Zhi He, D. Barajas-Solano, G. Tartakovsky, A. Tartakovsky
2019Advances in Water Resources385 citations Open access
Semantic Scholar
Physics‐Informed Neural Network Method for Forward and Backward Advection‐Dispersion Equations
Qi-Zhi He, A. Tartakovsky
2020Water Resources Research127 citations Open access
Semantic Scholar
Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks
A. Tartakovsky, Carlos Ortiz Marrero, P. Perdikaris, G. Tartakovsky +1 more
2018111 citations
Semantic Scholar
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
Xiu Yang, D. Barajas-Solano, G. Tartakovsky, A. Tartakovsky
2018Journal of Computational Physics93 citations Open access
Semantic Scholar
Learning Unknown Physics of non-Newtonian Fluids
B. Reyes, Amanda A. Howard, P. Perdikaris, A. Tartakovsky
2020Physical Review Fluids80 citations Open access
Semantic Scholar
Learning viscoelasticity models from indirect data using deep neural networks
Kailai Xu, A. Tartakovsky, J. Burghardt, Eric F Darve
2021Computer Methods in Applied Mechanics and Engineering79 citations Open access
Semantic Scholar
Physics-constrained deep neural network method for estimating parameters in a redox flow battery
Qi-Zhi He, Panos Stinis, A. Tartakovsky
2021Journal of Power Sources46 citations Open access
Semantic Scholar
Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs
Liu Yang, Prabhat, G. Karniadakis, Sean Treichler +7 more
2019Dynamic Languages Symposium46 citations Open access
Semantic Scholar
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations
R. Tipireddy, P. Perdikaris, Panos Stinis, A. Tartakovsky
2019arXiv.org38 citations
Semantic Scholar
Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence
Xiu Yang, G. Tartakovsky, A. Tartakovsky
2018arXiv.org37 citations
Semantic Scholar
Hydrodynamic dispersion in thin channels with micro-structured porous walls
B. Ling, M. Oostrom, A. Tartakovsky, I. Battiato
2018The Physics of Fluids36 citations
Semantic Scholar
Physics-informed machine learning with conditional Karhunen-Loève expansions
A. Tartakovsky, D. Barajas-Solano, Qi-Zhi He
2019Journal of Computational Physics35 citations Open access
Semantic Scholar
Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-Correlated Mechanical Input Power
W. S. Rosenthal, A. Tartakovsky, Zhen-Yu Huang
2018IEEE Transactions on Power Systems34 citations Open access
Semantic Scholar
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks
Panos Stinis, Tobias Hagge, A. Tartakovsky, Enoch Yeung
2018Journal of Computational Physics33 citations Open access
Semantic Scholar
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions
Yi-Fei Zong, Qi-Zhi He, A. Tartakovsky
2022Social Science Research Network31 citations Open access
Semantic Scholar
Urban Flood Modeling: Uncertainty Quantification and Physics‐Informed Gaussian Processes Regression Forecasting
Amir H. Kohanpur, S. Saksena, S. Dey, J. M. Johnson +3 more
2023Water Resources Research30 citations Open access
Semantic Scholar
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
Qi-Zhi He, Yu-Cheng Fu, Panos Stinis, A. Tartakovsky
2022Social Science Research Network29 citations Open access
Semantic Scholar
Physics-informed Gaussian process regression for states estimation and forecasting in power grids
A. Tartakovsky, Tong Ma, D. Barajas-Solano, R. Tipireddy
2022International Journal of Forecasting27 citations Open access
Semantic Scholar
Physics‐Informed Machine Learning Method for Large‐Scale Data Assimilation Problems
Yu-Hong Yeung, D. Barajas-Solano, A. Tartakovsky
2021Water Resources Research26 citations Open access
Semantic Scholar
A Deep Learning-Based Workflow for Fast Prediction of 3D State Variables in Geological Carbon Storage: A Dimension Reduction Approach
Hongsheng Wang, Seyyed A. Hosseini, A. Tartakovsky, J. Leng +1 more
2024Journal of Hydrology24 citations Open access
Semantic Scholar
A conservative level set method for N-phase flows with a free-energy-based surface tension model
Amanda A. Howard, A. Tartakovsky
2020Journal of Computational Physics24 citations Open access
Semantic Scholar
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chao-Peng Shen, A. Appling, P. Gentine, T. Bandai +25 more
2023arXiv.org22 citations Open access
Semantic Scholar
Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients
D. Barajas-Solano, A. Tartakovsky
2019Journal of Computational Physics22 citations Open access
Semantic Scholar
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models
R. Tipireddy, D. Barajas-Solano, A. Tartakovsky
2019Journal of Computational Physics21 citations Open access
Semantic Scholar
Uncertainty Quantification in Scale‐Dependent Models of Flow in Porous Media
A. Tartakovsky, M. Panzeri, G. Tartakovsky, A. Guadagnini
201721 citations Open access
Semantic Scholar
Physics-informed CoKriging model of a redox flow battery
Amanda A. Howard, A. Tartakovsky
2021Journal of Power Sources19 citations Open access
Semantic Scholar
Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning
Kailai Xu, A. Tartakovsky, J. Burghardt, Eric F Darve
2020arXiv.org19 citations
Semantic Scholar
Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems
D. Dylewsky, D. Barajas-Solano, Tong Ma, A. Tartakovsky +1 more
2020IEEE Access19 citations Open access
Semantic Scholar
Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems
Jing Li, A. Tartakovsky
2022Journal of Computational Physics17 citations Open access
Semantic Scholar
Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation
Yi-Fei Zong, D. Barajas-Solano, A. Tartakovsky
2024Computer Methods in Applied Mechanics and Engineering16 citations
Semantic Scholar
Bayesian reduced-order deep learning surrogate model for dynamic systems described by partial differential equations
Yuanzhe Wang, Yi-Fei Zong, James L. McCreight, Joseph D. Hughes +1 more
2024Computer Methods in Applied Mechanics and Engineering16 citations Open access
Semantic Scholar
Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids
A. Tartakovsky, R. Tipireddy
2018Hawaii International Conference on System Sciences16 citations Open access
Semantic Scholar
Physics Information Aided Kriging using Stochastic Simulation Models
Xiu Yang, G. Tartakovsky, A. Tartakovsky
2021SIAM Journal on Scientific Computing14 citations
Semantic Scholar
Comparison of surface tension generation methods in smoothed particle hydrodynamics for dynamic systems
E. Arai, A. Tartakovsky, R. Holt, S. Grace +1 more
202014 citations Open access
Semantic Scholar
Modeling electrokinetic flows by consistent implicit incompressible smoothed particle hydrodynamics
Wenxiao Pan, Kyungjoo Kim, M. Perego, A. Tartakovsky +1 more
2017Journal of Computational Physics14 citations Open access
Semantic Scholar
Physics-informed machine learning method with space-time Karhunen-Loève expansions for forward and inverse partial differential equations
A. Tartakovsky, Yi-Fei Zong
2023Journal of Computational Physics12 citations Open access
Semantic Scholar
Machine Learning in Heterogeneous Porous Materials
Marta D'Elia, H. Deng, C. Fraces, K. Garikipati +20 more
2022arXiv.org12 citations
Semantic Scholar
Multiscale Smoothed Particle Hydrodynamics Model Development for Simulating Preferential Flow Dynamics in Fractured Porous Media
E. Shigorina, Florian Rüdiger, A. Tartakovsky, M. Sauter +1 more
2020Water Resources Research12 citations Open access
Semantic Scholar
Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation
Jing Li, A. Tartakovsky
2019Journal of Computational Physics12 citations Open access
Semantic Scholar
Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets
Ahsan Jamil, Dale F. Rucker, Dan Lu, Scott C. Brooks +3 more
2024Journal of Applied Geophysics10 citations Open access
Semantic Scholar
Investigation of Gravity‐Driven Infiltration Instabilities in Smooth and Rough Fractures Using a Pairwise‐Force Smoothed Particle Hydrodynamics Model
E. Shigorina, A. Tartakovsky, Jannes Kordilla
2019Vadose Zone Journal10 citations Open access
Semantic Scholar
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
Tong Ma, Ren-Ke Huang, D. Barajas-Solano, R. Tipireddy +1 more
2019arXiv.org10 citations
Semantic Scholar
Stochastic Basis Adaptation and Spatial Domain Decomposition for Partial Differential Equations with Random Coefficients
R. Tipireddy, Panos Stinis, A. Tartakovsky
2018SIAM/ASA J. Uncertain. Quantification10 citations
Semantic Scholar
Dynamic mode decomposition for forecasting and analysis of power grid load data
D. Dylewsky, D. Barajas-Solano, Tong Ma, A. Tartakovsky +1 more
2020arXiv.org9 citations
Semantic Scholar
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems
Yi-Fei Zong, D. Barajas-Solano, A. Tartakovsky
2023Journal of Computational Physics8 citations
Semantic Scholar
Surface energy-driven ex situ hierarchical assembly of low-dimensional nanomaterials on graphene aerogels: a versatile strategy
Long Pan, Peiyuan Gao, E. Tervoort, A. Tartakovsky +1 more
20187 citations
Semantic Scholar
Conditional Karhunen–Loève regression model with Basis Adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling
Yu-Hong Yeung, R. Tipireddy, D. Barajas-Solano, A. Tartakovsky
2024Computer Methods in Applied Mechanics and Engineering6 citations Open access
Semantic Scholar
Non-local model for surface tension in fluid-fluid simulations
Amanda A. Howard, A. Tartakovsky
2019Journal of Computational Physics6 citations Open access
Semantic Scholar

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