Dynamics of Free Surfaces in Random Porous Media. - Daniel M. Tartakovsky; C. L. Winter (SIAM J. Appl. Math., 2001)

A Two-Scale Nonperturbative Approach to Uncertainty Analysis of Diffusion in Random Composites. - Dongbin Xiu; Daniel M. Tartakovsky (Multiscale Model. Simul., 2004)

Effective Properties of Random Composites. - Daniel M. Tartakovsky; Alberto Guadagnini (SIAM J. Sci. Comput., 2004)

Noise in algorithm refinement methods. - Francis J. Alexander; Daniel M. Tartakovsky; Alejandro L. Garcia (Comput. Sci. Eng., 2005)

Guest Editors' Introduction: Multiphysics Modeling. - Daniel M. Tartakovsky; Francis J. Alexander (Comput. Sci. Eng., 2005)

Stochastic analysis of transport in tubes with rough walls. - Daniel M. Tartakovsky; Dongbin Xiu (J. Comput. Phys., 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)

Numerical Methods for Differential Equations in Random Domains. - Dongbin Xiu; Daniel M. Tartakovsky (SIAM J. Sci. Comput., 2006)

Subsurface characterization with support vector machines. - Brendt Wohlberg; Daniel M. Tartakovsky; Alberto Guadagnini (IEEE Trans. Geosci. Remote. Sens., 2006)

Guest Editors' Introduction: Stochastic Modeling of Complex Systems. - Daniel M. Tartakovsky; Dongbin Xiu (Comput. Sci. Eng., 2007)

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)

Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids. - Guang Lin 001; Alexandre M. Tartakovsky; Daniel M. Tartakovsky (J. Comput. Phys., 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 in kinematic-wave models. - Peng Wang 022; Daniel M. Tartakovsky (J. Comput. Phys., 2012)

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 (BIBM, 2013)

Particle-tracking simulations of anomalous transport in hierarchically fractured rocks. - Delphine Roubinet; Jean-Raynald de Dreuzy; Daniel M. Tartakovsky (Comput. Geosci., 2013)

Exact PDF equations and closure approximations for advective-reactive transport. - Daniele Venturi 002; Daniel M. Tartakovsky; Alexandre M. Tartakovsky; George E. Karniadakis (J. Comput. Phys., 2013)

CDF Solutions of Buckley-Leverett Equation with Uncertain Parameters. - Peng Wang 022; Daniel M. Tartakovsky; K. D. Jarman Jr.; Alexandre M. Tartakovsky (Multiscale Model. Simul., 2013)

A New Physiological Boundary Condition for Hemodynamics. - Will Cousins; Pierre-Alain Gremaud; Daniel M. Tartakovsky (SIAM J. Appl. Math., 2013)

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)

Noise propagation in hybrid models of nonlinear systems: The Ginzburg-Landau equation. - Søren Taverniers; Francis J. Alexander; Daniel M. Tartakovsky (J. Comput. Phys., 2014)

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)

Impact of Data Assimilation on Cost-Accuracy Tradeoff in Multifidelity Models. - Michael Sinsbeck; Daniel M. Tartakovsky (SIAM/ASA J. Uncertain. Quantification, 2015)

Conservative tightly-coupled simulations of stochastic multiscale systems. - Søren Taverniers; Alexander Y. Pigarov; Daniel M. Tartakovsky (J. Comput. Phys., 2016)

Stochastic Collocation Methods for Nonlinear Parabolic Equations with Random Coefficients. - David A. Barajas-Solano; Daniel M. Tartakovsky (SIAM/ASA J. Uncertain. Quantification, 2016)

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 (ECCTD, 2017)

On the use of reverse Brownian motion to accelerate hybrid simulations. - Joseph Bakarji; 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)

Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor. - Søren Taverniers; Daniel M. Tartakovsky (J. Comput. Phys., 2017)

Doubly Penalized LASSO for Reconstruction of Biological Networks. - Behrang Asadi; Mano Ram Maurya; Daniel M. Tartakovsky; Shankar Subramaniam (Proc. IEEE, 2017)

Parallel tensor methods for high-dimensional linear PDEs. - Arnout M. P. Boelens; Daniele Venturi 002; Daniel M. Tartakovsky (J. Comput. Phys., 2018)

Nonlocal PDF methods for Langevin equations with colored noise. - Tyler Maltba; Pierre-Alain Gremaud; Daniel M. Tartakovsky (J. Comput. Phys., 2018)

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)

Markov Chain Monte Carlo with Neural Network Surrogates: Application to Contaminant Source Identification. - Zitong Zhou; 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)

Autonomous learning of nonlocal stochastic neuron dynamics. - Tyler E. Maltba; Hongli Zhao; Daniel M. Tartakovsky (CoRR, 2020)

Tensor methods for the Boltzmann-BGK equation. - Arnout M. P. Boelens; Daniele Venturi 002; 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)

Lagrangian dynamic mode decomposition for construction of reduced-order models of advection-dominated phenomena. - Hannah Lu; Daniel M. Tartakovsky (J. Comput. Phys., 2020)

Estimation of distributions via multilevel Monte Carlo with stratified sampling. - Søren Taverniers; Daniel M. Tartakovsky (J. Comput. Phys., 2020)

Resource-Constrained Model Selection for Uncertainty Propagation and Data Assimilation. - Lun Yang; Peng Wang 022; Daniel M. Tartakovsky (SIAM/ASA J. Uncertain. Quantification, 2020)

Data-Informed Method of Distributions for Hyperbolic Conservation Laws. - Francesca Boso; Daniel M. Tartakovsky (SIAM J. Sci. Comput., 2020)

Prediction Accuracy of Dynamic Mode Decomposition. - Hannah Lu; Daniel M. Tartakovsky (SIAM J. Sci. Comput., 2020)

Graph-Informed Neural Networks. - Søren Taverniers; Eric Joseph Hall; Markos A. Katsoulakis; Daniel M. Tartakovsky (AAAI Spring Symposium: MLPS, 2021)

Transfer Learning on Multi-Fidelity Data. - Dong H. Song; Daniel M. Tartakovsky (CoRR, 2021)

Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties. - Zitong Zhou; Nicholas Zabaras; Daniel M. Tartakovsky (CoRR, 2021)

Data-driven discovery of coarse-grained equations. - Joseph Bakarji; 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)

Extended dynamic mode decomposition for inhomogeneous problems. - Hannah Lu; Daniel M. Tartakovsky (J. Comput. Phys., 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)

Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs. - Francesca Boso; Dimitris Boskos; Jorge Cortés 001; Sonia Martínez; Daniel M. Tartakovsky (SIAM J. Sci. Comput., 2021)

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 (SSCI, 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; et al. (CoRR, 2022)

Model Reduction via Dynamic Mode Decomposition. - Hannah Lu; Daniel M. Tartakovsky (CoRR, 2022)

Feature-Informed Data Assimilation - Definitions and Illustrative Examples. - Wei Kang; Daniel M. Tartakovsky; Apoorv Srivastava (CoRR, 2022)

Information geometry of physics-informed statistical manifolds and its use in data assimilation. - Francesca Boso; Daniel M. Tartakovsky (J. Comput. Phys., 2022)

Polynomial Chaos Expansions for Stiff Random ODEs. - Wenjie Shi; Daniel M. Tartakovsky (SIAM J. Sci. Comput., 2022)

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)

Learning Nonautonomous Systems via Dynamic Mode Decomposition. - Hannah Lu; 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)

Physics-Aware Reduced-Order Modeling of Nonautonomous Advection-Dominated Problems. - Hongli Zhao; Daniel M. Tartakovsky (CoRR, 2023)

Parsimonious models of in-host viral dynamics and immune response. - Hannah Lu; Francesco Giannino; Daniel M. Tartakovsky (Appl. Math. Lett., 2023)

DRIPS: A framework for dimension reduction and interpolation in parameter space. - Hannah Lu; Daniel M. Tartakovsky (J. Comput. Phys., 2023)

Feature-informed data assimilation. - Apoorv Srivastava; Wei Kang; Daniel M. Tartakovsky (J. Comput. Phys., 2023)

Neural Oscillators for Generalization of Physics-Informed Machine Learning. - Taniya Kapoor; Abhishek Chandra; Daniel M. Tartakovsky; Hongrui Wang 001; Alfredo Núñez; Rolf P. B. J. Dollevoet (AAAI, 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)

Baseflow identification via explainable AI with Kolmogorov-Arnold networks. - Chuyang Liu; Tirthankar Roy; Daniel M. Tartakovsky; Dipankar Dwivedi (CoRR, 2024)

Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling. - Adrienne M. Propp; Daniel M. Tartakovsky (CoRR, 2024)

Efficient quadratures for high-dimensional Bayesian data assimilation. - Ming Cheng; Peng Wang 022; Daniel M. Tartakovsky (J. Comput. Phys., 2024)

High-order Lagrangian algorithms for Liouville models of particle-laden flows. - Daniel Domínguez-Vázquez; Sergio A. Castiblanco-Ballesteros; Gustaaf B. Jacobs; Daniel M. Tartakovsky (J. Comput. Phys., 2024)

Data-driven models of nonautonomous systems. - Hannah Lu; Daniel M. Tartakovsky (J. Comput. Phys., 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., 2024)

Generalizable models of magnetic hysteresis via physics-aware recurrent neural networks. - Abhishek Chandra; Taniya Kapoor; Bram Daniels; Mitrofan Curti; Koen Tiels; Daniel M. Tartakovsky; Elena A. Lomonova (Comput. Phys. Commun., 2025)

Discovery of Linear Representations for Nonautonomous Translation-Invariant Problems. - Hongli Zhao; Daniel M. Tartakovsky (SIAM J. Sci. Comput., 2025)

Neural Differential Equation-Based Two-Stage Approach for Generalization of Beam Dynamics. - Taniya Kapoor; Hongrui Wang 001; Anastasios Stamou; Kareem El Sayed; Alfredo Núñez; Daniel M. Tartakovsky; Rolf P. B. J. Dollevoet (IEEE Trans. Ind. Informatics, 2025)

Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs. - Adrienne M. Propp; Jonas A. Actor; Elise Walker; Houman Owhadi; Nathaniel Trask; Daniel M. Tartakovsky (CoRR, 2025)