Daniel M. Tartakovsky

Publication Activity (10 Years)

Years Active: 2001-2024
Publications (10 Years): 54

Top Topics

Conservation Laws

Data Assimilation

Top Venues

J. Comput. Phys.

SIAM J. Sci. Comput.

SIAM/ASA J. Uncertain. Quantification

Publications

Hannah Lu, Daniel M. Tartakovsky
Data-driven models of nonautonomous systems. J. Comput. Phys. 507 (2024)
Taniya Kapoor, Abhishek Chandra, Daniel M. Tartakovsky, Hongrui Wang, Alfredo Núñez, Rolf P. B. J. Dollevoet
Neural Oscillators for Generalization of Physics-Informed Machine Learning. AAAI (2024)
Guofeng Song, Delphine Roubinet, Xiaoguang Wang, Gensheng Li, Xianzhi Song, Daniel M. Tartakovsky
Surrogate models of heat transfer in fractured rock and their use in parameter estimation. Comput. Geosci. 183 (2024)
Ressi Bonti Muhammad, Apoorv Srivastava, Sergey Alyaev, Reidar Brumer Bratvold, Daniel M. Tartakovsky
High-Precision Geosteering via Reinforcement Learning and Particle Filters. CoRR (2024)
Ming Cheng, Peng Wang, Daniel M. Tartakovsky
Efficient quadratures for high-dimensional Bayesian data assimilation. J. Comput. Phys. 506 (2024)
Daniel Domínguez-Vázquez, Sergio A. Castiblanco-Ballesteros, Gustaaf B. Jacobs, Daniel M. Tartakovsky
High-order Lagrangian algorithms for Liouville models of particle-laden flows. J. Comput. Phys. 515 (2024)
Hannah Lu, Daniel M. Tartakovsky
DRIPS: A framework for dimension reduction and interpolation in parameter space. J. Comput. Phys. 493 (2023)
Hannah Lu, Francesco Giannino, Daniel M. Tartakovsky
Parsimonious models of in-host viral dynamics and immune response. Appl. Math. Lett. 145 (2023)
Abhishek Chandra, Taniya Kapoor, Bram Daniels, Mitrofan Curti, Koen Tiels, Daniel M. Tartakovsky, Elena A. Lomonova
Neural oscillators for magnetic hysteresis modeling. CoRR (2023)
Hannah Lu, Daniel M. Tartakovsky
Learning Nonautonomous Systems via Dynamic Mode Decomposition. CoRR (2023)
Hongli Zhao, Daniel M. Tartakovsky
Physics-Aware Reduced-Order Modeling of Nonautonomous Advection-Dominated Problems. CoRR (2023)
Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, Daniel M. Tartakovsky
Discovering Sparse Hysteresis Models: A Data-driven Study for Piezoelectric Materials and Perspectives on Magnetic Hysteresis. CoRR (2023)
Apoorv Srivastava, Wei Kang, Daniel M. Tartakovsky
Feature-informed data assimilation. J. Comput. Phys. 494 (2023)
Taniya Kapoor, Abhishek Chandra, Daniel M. Tartakovsky, Hongrui Wang, Alfredo Núñez, Rolf P. B. J. Dollevoet
Neural oscillators for generalization of physics-informed machine learning. CoRR (2023)
Wei Kang, Daniel M. Tartakovsky, Apoorv Srivastava
Feature-Informed Data Assimilation - Definitions and Illustrative Examples. CoRR (2022)
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)
Hannah Lu, Daniel M. Tartakovsky
Model Reduction via Dynamic Mode Decomposition. CoRR (2022)
Wenjie Shi, Daniel M. Tartakovsky
Polynomial Chaos Expansions for Stiff Random ODEs. SIAM J. Sci. Comput. 44 (3) (2022)
Francesca Boso, Daniel M. Tartakovsky
Information geometry of physics-informed statistical manifolds and its use in data assimilation. J. Comput. Phys. 467 (2022)
Abhishek Chandra, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, Daniel M. Tartakovsky
Physics-informed neural networks for modelling anisotropic and bi-anisotropic electromagnetic constitutive laws through indirect data. SSCI (2022)
Hannah Lu, Daniel M. Tartakovsky
Extended dynamic mode decomposition for inhomogeneous problems. J. Comput. Phys. 444 (2021)
Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky
Graph-Informed Neural Networks. AAAI Spring Symposium: MLPS (2021)
Zitong Zhou, Nicholas Zabaras, Daniel M. Tartakovsky
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties. CoRR (2021)
Eric Joseph Hall, Søren Taverniers, Markos A. Katsoulakis, Daniel M. Tartakovsky
GINNs: Graph-Informed Neural Networks for multiscale physics. J. Comput. Phys. 433 (2021)
Joseph Bakarji, Daniel M. Tartakovsky
Data-driven discovery of coarse-grained equations. J. Comput. Phys. 434 (2021)
Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky
Mutual information for explainable deep learning of multiscale systems. J. Comput. Phys. 444 (2021)
Dong H. Song, Daniel M. Tartakovsky
Transfer Learning on Multi-Fidelity Data. CoRR (2021)
Francesca Boso, Dimitris Boskos, Jorge Cortés, Sonia Martínez, Daniel M. Tartakovsky
Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs. SIAM J. Sci. Comput. 43 (3) (2021)
Tyler E. Maltba, Hongli Zhao, Daniel M. Tartakovsky
Autonomous learning of nonlocal stochastic neuron dynamics. CoRR (2020)
Arnout M. P. Boelens, Daniele Venturi, Daniel M. Tartakovsky
Tensor methods for the Boltzmann-BGK equation. J. Comput. Phys. 421 (2020)
Søren Taverniers, Daniel M. Tartakovsky
Estimation of distributions via multilevel Monte Carlo with stratified sampling. J. Comput. Phys. 419 (2020)
Chunsong Kwon, Daniel M. Tartakovsky
Modified immersed boundary method for flows over randomly rough surfaces. J. Comput. Phys. 406 (2020)
Hannah Lu, Daniel M. Tartakovsky
Prediction Accuracy of Dynamic Mode Decomposition. SIAM J. Sci. Comput. 42 (3) (2020)
Zitong Zhou, Daniel M. Tartakovsky
Markov Chain Monte Carlo with Neural Network Surrogates: Application to Contaminant Source Identification. CoRR (2020)
Lun Yang, Peng Wang, Daniel M. Tartakovsky
Resource-Constrained Model Selection for Uncertainty Propagation and Data Assimilation. SIAM/ASA J. Uncertain. Quantification 8 (3) (2020)
Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky
Mutual Information for Explainable Deep Learning of Multiscale Systems. CoRR (2020)
Eric Joseph Hall, Søren Taverniers, Markos A. Katsoulakis, Daniel M. Tartakovsky
GINNs: Graph-Informed Neural Networks for Multiscale Physics. CoRR (2020)
Hannah Lu, Daniel M. Tartakovsky
Dynamic Mode Decomposition for Construction of Reduced-Order Models of Hyperbolic Problems with Shocks. CoRR (2020)
Hannah Lu, Daniel M. Tartakovsky
Extended Dynamic Mode Decomposition for Inhomogeneous Problems. CoRR (2020)
Francesca Boso, Daniel M. Tartakovsky
Data-Informed Method of Distributions for Hyperbolic Conservation Laws. SIAM J. Sci. Comput. 42 (1) (2020)
Hannah Lu, Daniel M. Tartakovsky
Lagrangian dynamic mode decomposition for construction of reduced-order models of advection-dominated phenomena. J. Comput. Phys. 407 (2020)
Joseph Bakarji, Daniel M. Tartakovsky
Data-Driven Discovery of Coarse-Grained Equations. CoRR (2020)
Kimoon Um, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky
Causality and Bayesian Network PDEs for multiscale representations of porous media. J. Comput. Phys. 394 (2019)
Søren Taverniers, Daniel M. Tartakovsky
Estimation of distributions via multilevel Monte Carlo with stratified sampling. CoRR (2019)
Hannah Lu, Daniel M. Tartakovsky
Lagrangian Dynamic Mode Decomposition for Construction of Reduced-Order Models of Advection-Dominated Phenomena. CoRR (2019)
Arnout M. P. Boelens, Daniele Venturi, Daniel M. Tartakovsky
Parallel tensor methods for high-dimensional linear PDEs. J. Comput. Phys. 375 (2018)
Tyler Maltba, Pierre-Alain Gremaud, Daniel M. Tartakovsky
Nonlocal PDF methods for Langevin equations with colored noise. J. Comput. Phys. 367 (2018)
Joseph Bakarji, Daniel M. Tartakovsky
On the use of reverse Brownian motion to accelerate hybrid simulations. J. Comput. Phys. 334 (2017)
Søren Taverniers, Daniel M. Tartakovsky
A tightly-coupled domain-decomposition approach for highly nonlinear stochastic multiphysics systems. J. Comput. Phys. 330 (2017)
Behrang Asadi, Mano Ram Maurya, Daniel M. Tartakovsky, Shankar Subramaniam
Doubly Penalized LASSO for Reconstruction of Biological Networks. Proc. IEEE 105 (2) (2017)
Søren Taverniers, Daniel M. Tartakovsky
Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor. J. Comput. Phys. 348 (2017)
Ilenia Battiato, Daniel M. Tartakovsky, Pedro J. Cabrales, Marcos Intaglietta
Role of glycocalyx in attenuation of shear stress on endothelial cells: From in vivo experiments to microfluidic circuits. ECCTD (2017)
David A. Barajas-Solano, Daniel M. Tartakovsky
Stochastic Collocation Methods for Nonlinear Parabolic Equations with Random Coefficients. SIAM/ASA J. Uncertain. Quantification 4 (1) (2016)
Søren Taverniers, Alexander Y. Pigarov, Daniel M. Tartakovsky
Conservative tightly-coupled simulations of stochastic multiscale systems. J. Comput. Phys. 313 (2016)
Sang-Woo Park, Marcos Intaglietta, Daniel M. Tartakovsky
Impact of stochastic fluctuations in the cell free layer on nitric oxide bioavailability. Frontiers Comput. Neurosci. 9 (2015)
Michael Sinsbeck, Daniel M. Tartakovsky
Impact of Data Assimilation on Cost-Accuracy Tradeoff in Multifidelity Models. SIAM/ASA J. Uncertain. Quantification 3 (1) (2015)
Farzaneh Farhangmehr, Mano Ram Maurya, Daniel M. Tartakovsky, Shankar Subramaniam
Information theoretic approach to complex biological network reconstruction: application to cytokine release in RAW 264.7 macrophages. BMC Syst. Biol. 8 (2014)
Søren Taverniers, Francis J. Alexander, Daniel M. Tartakovsky
Noise propagation in hybrid models of nonlinear systems: The Ginzburg-Landau equation. J. Comput. Phys. 262 (2014)
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)
Will Cousins, Pierre-Alain Gremaud, Daniel M. Tartakovsky
A New Physiological Boundary Condition for Hemodynamics. SIAM J. Appl. Math. 73 (3) (2013)
Delphine Roubinet, Jean-Raynald de Dreuzy, Daniel M. Tartakovsky
Particle-tracking simulations of anomalous transport in hierarchically fractured rocks. Comput. Geosci. 50 (2013)
Farzaneh Farhangmehr, Daniel M. Tartakovsky, Parastou Sadatmousavi, Mano Ram Maurya, Shankar Subramaniam
An information-theoretic algorithm to data-driven genetic pathway interaction network reconstruction of dynamic systems. BIBM (2013)
Peng Wang, Daniel M. Tartakovsky
Uncertainty quantification in kinematic-wave models. J. Comput. Phys. 231 (23) (2012)
Behrang Asadi, Daniel M. Tartakovsky, Mano Ram Maurya, Shankar Subramaniam
Doubly Penalized LASSO for Reconstruction of Biological Networks. HISB (2012)
Gowri Srinivasan, Daniel M. Tartakovsky, Marco Dentz, Hari S. Viswanathan, Brian Berkowitz, B. A. Robinson
Random walk particle tracking simulations of non-Fickian transport in heterogeneous media. J. Comput. Phys. 229 (11) (2010)
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)
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)
Daniel M. Tartakovsky, Dongbin Xiu
Guest Editors' Introduction: Stochastic Modeling of Complex Systems. Comput. Sci. Eng. 9 (2) (2007)
C. L. Winter, Alberto Guadagnini, Douglas W. Nychka, Daniel M. Tartakovsky
Multivariate sensitivity analysis of saturated flow through simulated highly heterogeneous groundwater aquifers. J. Comput. Phys. 217 (1) (2006)
Daniel M. Tartakovsky, Dongbin Xiu
Stochastic analysis of transport in tubes with rough walls. J. Comput. Phys. 217 (1) (2006)
Dongbin Xiu, Daniel M. Tartakovsky
Numerical Methods for Differential Equations in Random Domains. SIAM J. Sci. Comput. 28 (3) (2006)
Brendt Wohlberg, Daniel M. Tartakovsky, Alberto Guadagnini
Subsurface characterization with support vector machines. IEEE Trans. Geosci. Remote. Sens. 44 (1) (2006)
Francis J. Alexander, Daniel M. Tartakovsky, Alejandro L. Garcia
Noise in algorithm refinement methods. Comput. Sci. Eng. 7 (3) (2005)
Daniel M. Tartakovsky, Francis J. Alexander
Guest Editors' Introduction: Multiphysics Modeling. Comput. Sci. Eng. 7 (3) (2005)
Daniel M. Tartakovsky, Alberto Guadagnini
Effective Properties of Random Composites. SIAM J. Sci. Comput. 26 (2) (2004)
Dongbin Xiu, Daniel M. Tartakovsky
A Two-Scale Nonperturbative Approach to Uncertainty Analysis of Diffusion in Random Composites. Multiscale Model. Simul. 2 (4) (2004)
Daniel M. Tartakovsky, C. L. Winter
Dynamics of Free Surfaces in Random Porous Media. SIAM J. Appl. Math. 61 (6) (2001)