Daniel M. Tartakovsky

According to our database, Daniel M. Tartakovsky authored at least 67 papers between 2001 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Surrogate models of heat transfer in fractured rock and their use in parameter estimation.

Guofeng Song, Delphine Roubinet, Xiaoguang Wang, Gensheng Li, Xianzhi Song, Daniel M. Tartakovsky

Comput. Geosci., January, 2024

High-Precision Geosteering via Reinforcement Learning and Particle Filters.

Ressi Bonti Muhammad, Apoorv Srivastava, Sergey Alyaev, Reidar Brumer Bratvold, Daniel M. Tartakovsky

CoRR, 2024

Neural Oscillators for Generalization of Physics-Informed Machine Learning.

Taniya Kapoor, Abhishek Chandra, Daniel M. Tartakovsky, Hongrui Wang, Alfredo Núñez, Rolf P. B. J. Dollevoet

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Feature-informed data assimilation.

Apoorv Srivastava, Wei Kang, Daniel M. Tartakovsky

J. Comput. Phys., December, 2023

DRIPS: A framework for dimension reduction and interpolation in parameter space.

Hannah Lu, Daniel M. Tartakovsky

J. Comput. Phys., November, 2023

Parsimonious models of in-host viral dynamics and immune response.

Hannah Lu, Francesco Giannino, Daniel M. Tartakovsky

Appl. Math. Lett., November, 2023

Physics-Aware Reduced-Order Modeling of Nonautonomous Advection-Dominated Problems.

Hongli Zhao, Daniel M. Tartakovsky

CoRR, 2023

Neural oscillators for magnetic hysteresis modeling.

Abhishek Chandra, Taniya Kapoor, Bram Daniels, Mitrofan Curti, Koen Tiels, Daniel M. Tartakovsky, Elena A. Lomonova

CoRR, 2023

Learning Nonautonomous Systems via Dynamic Mode Decomposition.

Hannah Lu, Daniel M. Tartakovsky

CoRR, 2023

Discovering Sparse Hysteresis Models: A Data-driven Study for Piezoelectric Materials and Perspectives on Magnetic Hysteresis.

Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, Daniel M. Tartakovsky

CoRR, 2023

2022

Polynomial Chaos Expansions for Stiff Random ODEs.

Wenjie Shi, Daniel M. Tartakovsky

SIAM J. Sci. Comput., 2022

Information geometry of physics-informed statistical manifolds and its use in data assimilation.

Francesca Boso, Daniel M. Tartakovsky

J. Comput. Phys., 2022

Feature-Informed Data Assimilation - Definitions and Illustrative Examples.

Wei Kang, Daniel M. Tartakovsky, Apoorv Srivastava

CoRR, 2022

Model Reduction via Dynamic Mode Decomposition.

Hannah Lu, Daniel M. Tartakovsky

CoRR, 2022

Machine Learning in Heterogeneous Porous Materials.

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

CoRR, 2022

Physics-informed neural networks for modelling anisotropic and bi-anisotropic electromagnetic constitutive laws through indirect data.

Abhishek Chandra, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, Daniel M. Tartakovsky

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2022

2021

Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs.

Francesca Boso, Dimitris Boskos, Jorge Cortés, Sonia Martínez, Daniel M. Tartakovsky

SIAM J. Sci. Comput., 2021

Mutual information for explainable deep learning of multiscale systems.

Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky

J. Comput. Phys., 2021

Extended dynamic mode decomposition for inhomogeneous problems.

Hannah Lu, Daniel M. Tartakovsky

J. Comput. Phys., 2021

GINNs: Graph-Informed Neural Networks for multiscale physics.

Eric Joseph Hall, Søren Taverniers, Markos A. Katsoulakis, Daniel M. Tartakovsky

J. Comput. Phys., 2021

Data-driven discovery of coarse-grained equations.

Joseph Bakarji, Daniel M. Tartakovsky

J. Comput. Phys., 2021

Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties.

Zitong Zhou, Nicholas Zabaras, Daniel M. Tartakovsky

CoRR, 2021

Transfer Learning on Multi-Fidelity Data.

Dong H. Song, Daniel M. Tartakovsky

CoRR, 2021

Graph-Informed Neural Networks.

Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky

Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

2020

Prediction Accuracy of Dynamic Mode Decomposition.

Hannah Lu, Daniel M. Tartakovsky

SIAM J. Sci. Comput., 2020

Data-Informed Method of Distributions for Hyperbolic Conservation Laws.

Francesca Boso, Daniel M. Tartakovsky

SIAM J. Sci. Comput., 2020

Resource-Constrained Model Selection for Uncertainty Propagation and Data Assimilation.

Lun Yang, Peng Wang, Daniel M. Tartakovsky

SIAM/ASA J. Uncertain. Quantification, 2020

Estimation of distributions via multilevel Monte Carlo with stratified sampling.

Søren Taverniers, Daniel M. Tartakovsky

J. Comput. Phys., 2020

Lagrangian dynamic mode decomposition for construction of reduced-order models of advection-dominated phenomena.

Hannah Lu, Daniel M. Tartakovsky

J. Comput. Phys., 2020

Modified immersed boundary method for flows over randomly rough surfaces.

Chunsong Kwon, Daniel M. Tartakovsky

J. Comput. Phys., 2020

Tensor methods for the Boltzmann-BGK equation.

Arnout M. P. Boelens, Daniele Venturi, Daniel M. Tartakovsky

J. Comput. Phys., 2020

Autonomous learning of nonlocal stochastic neuron dynamics.

Tyler E. Maltba, Hongli Zhao, Daniel M. Tartakovsky

CoRR, 2020

Dynamic Mode Decomposition for Construction of Reduced-Order Models of Hyperbolic Problems with Shocks.

Hannah Lu, Daniel M. Tartakovsky

CoRR, 2020

Markov Chain Monte Carlo with Neural Network Surrogates: Application to Contaminant Source Identification.

Zitong Zhou, Daniel M. Tartakovsky

CoRR, 2020

2019

Causality and Bayesian Network PDEs for multiscale representations of porous media.

Kimoon Um, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky

J. Comput. Phys., 2019

2018

Nonlocal PDF methods for Langevin equations with colored noise.

Tyler Maltba, Pierre-Alain Gremaud, Daniel M. Tartakovsky

J. Comput. Phys., 2018

Parallel tensor methods for high-dimensional linear PDEs.

Arnout M. P. Boelens, Daniele Venturi, Daniel M. Tartakovsky

J. Comput. Phys., 2018

2017

Doubly Penalized LASSO for Reconstruction of Biological Networks.

Behrang Asadi, Mano Ram Maurya, Daniel M. Tartakovsky, Shankar Subramaniam

Proc. IEEE, 2017

Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor.

Søren Taverniers, Daniel M. Tartakovsky

J. Comput. Phys., 2017

A tightly-coupled domain-decomposition approach for highly nonlinear stochastic multiphysics systems.

Søren Taverniers, Daniel M. Tartakovsky

J. Comput. Phys., 2017

On the use of reverse Brownian motion to accelerate hybrid simulations.

Joseph Bakarji, Daniel M. Tartakovsky

J. Comput. Phys., 2017

Role of glycocalyx in attenuation of shear stress on endothelial cells: From in vivo experiments to microfluidic circuits.

Ilenia Battiato, Daniel M. Tartakovsky, Pedro J. Cabrales, Marcos Intaglietta

Proceedings of the 2017 European Conference on Circuit Theory and Design, 2017

2016

Stochastic Collocation Methods for Nonlinear Parabolic Equations with Random Coefficients.

David A. Barajas-Solano, Daniel M. Tartakovsky

SIAM/ASA J. Uncertain. Quantification, 2016

Conservative tightly-coupled simulations of stochastic multiscale systems.

Søren Taverniers, Alexander Y. Pigarov, Daniel M. Tartakovsky

J. Comput. Phys., 2016

2015

Impact of Data Assimilation on Cost-Accuracy Tradeoff in Multifidelity Models.

Michael Sinsbeck, Daniel M. Tartakovsky

SIAM/ASA J. Uncertain. Quantification, 2015

Impact of stochastic fluctuations in the cell free layer on nitric oxide bioavailability.

Sang-Woo Park, Marcos Intaglietta, Daniel M. Tartakovsky

Frontiers Comput. Neurosci., 2015

2014

Noise propagation in hybrid models of nonlinear systems: The Ginzburg-Landau equation.

Søren Taverniers, Francis J. Alexander, Daniel M. Tartakovsky

J. Comput. Phys., 2014

Information theoretic approach to complex biological network reconstruction: application to cytokine release in RAW 264.7 macrophages.

Farzaneh Farhangmehr, Mano Ram Maurya, Daniel M. Tartakovsky, Shankar Subramaniam

BMC Syst. Biol., 2014

2013

A New Physiological Boundary Condition for Hemodynamics.

Will Cousins, Pierre-Alain Gremaud, Daniel M. Tartakovsky

SIAM J. Appl. Math., 2013

CDF Solutions of Buckley-Leverett Equation with Uncertain Parameters.

Peng Wang, Daniel M. Tartakovsky, K. D. Jarman Jr., Alexandre M. Tartakovsky

Multiscale Model. Simul., 2013

Exact PDF equations and closure approximations for advective-reactive transport.

Daniele Venturi, Daniel M. Tartakovsky, Alexandre M. Tartakovsky, George E. Karniadakis

J. Comput. Phys., 2013

Particle-tracking simulations of anomalous transport in hierarchically fractured rocks.

Delphine Roubinet, Jean-Raynald de Dreuzy, Daniel M. Tartakovsky

Comput. Geosci., 2013

An information-theoretic algorithm to data-driven genetic pathway interaction network reconstruction of dynamic systems.

Farzaneh Farhangmehr, Daniel M. Tartakovsky, Parastou Sadatmousavi, Mano Ram Maurya, Shankar Subramaniam

Proceedings of the 2013 IEEE International Conference on Bioinformatics and Biomedicine, 2013

2012

Uncertainty quantification in kinematic-wave models.

Peng Wang, Daniel M. Tartakovsky

J. Comput. Phys., 2012

2010

Random walk particle tracking simulations of non-Fickian transport in heterogeneous media.

Gowri Srinivasan, Daniel M. Tartakovsky, Marco Dentz, Hari S. Viswanathan, Brian Berkowitz, B. A. Robinson

J. Comput. Phys., 2010

Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids.

G. Lin, Alexandre M. Tartakovsky, Daniel M. Tartakovsky

J. Comput. Phys., 2010

2008

Hybrid Simulations of Reaction-Diffusion Systems in Porous Media.

Alexandre M. Tartakovsky, Daniel M. Tartakovsky, Timothy D. Scheibe, Paul Meakin

SIAM J. Sci. Comput., 2008

2007

Guest Editors' Introduction: Stochastic Modeling of Complex Systems.

Daniel M. Tartakovsky, Dongbin Xiu

Comput. Sci. Eng., 2007

2006

Subsurface characterization with support vector machines.

Brendt Wohlberg, Daniel M. Tartakovsky, Alberto Guadagnini

IEEE Trans. Geosci. Remote. Sens., 2006

Numerical Methods for Differential Equations in Random Domains.

Dongbin Xiu, Daniel M. Tartakovsky

SIAM J. Sci. Comput., 2006

Multivariate sensitivity analysis of saturated flow through simulated highly heterogeneous groundwater aquifers.

C. L. Winter, Alberto Guadagnini, Douglas W. Nychka, Daniel M. Tartakovsky

J. Comput. Phys., 2006

Stochastic analysis of transport in tubes with rough walls.

Daniel M. Tartakovsky, Dongbin Xiu

J. Comput. Phys., 2006

2005

Guest Editors' Introduction: Multiphysics Modeling.

Daniel M. Tartakovsky, Francis J. Alexander

Comput. Sci. Eng., 2005

Noise in algorithm refinement methods.

Francis J. Alexander, Daniel M. Tartakovsky, Alejandro L. Garcia

Comput. Sci. Eng., 2005

2004

Effective Properties of Random Composites.

Daniel M. Tartakovsky, Alberto Guadagnini

SIAM J. Sci. Comput., 2004

A Two-Scale Nonperturbative Approach to Uncertainty Analysis of Diffusion in Random Composites.

Dongbin Xiu, Daniel M. Tartakovsky

Multiscale Model. Simul., 2004

2001

Dynamics of Free Surfaces in Random Porous Media.

Daniel M. Tartakovsky, C. L. Winter

SIAM J. Appl. Math., 2001