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Daniel M. Tartakovsky
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2020 – today
- 2024
- [j52]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: 105509 (2024) - [j51]Ming Cheng
Efficient quadratures for high-dimensional Bayesian data assimilation. J. Comput. Phys. 506: 112945 (2024) - [j50]Hannah Lu, Daniel M. Tartakovsky
Data-driven models of nonautonomous systems. J. Comput. Phys. 507: 112976 (2024) - [j49]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: 113281 (2024) - [c6]Taniya Kapoor
Neural Oscillators for Generalization of Physics-Informed Machine Learning. AAAI 2024: 13059-13067 - [i22]Ressi Bonti Muhammad, Apoorv Srivastava, Sergey Alyaev, Reidar Brumer Bratvold, Daniel M. Tartakovsky:
High-Precision Geosteering via Reinforcement Learning and Particle Filters. CoRR abs/2402.06377 (2024) - [i21]Chuyang Liu, Tirthankar Roy, Daniel M. Tartakovsky, Dipankar Dwivedi:
Baseflow identification via explainable AI with Kolmogorov-Arnold networks. CoRR abs/2410.11587 (2024) - [i20]Adrienne M. Propp, Daniel M. Tartakovsky:
Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling. CoRR abs/2410.12241 (2024) - 2023
- [j48]Hannah Lu, Francesco Giannino
Parsimonious models of in-host viral dynamics and immune response. Appl. Math. Lett. 145: 108781 (2023) - [j47]Hannah Lu, Daniel M. Tartakovsky
DRIPS: A framework for dimension reduction and interpolation in parameter space. J. Comput. Phys. 493: 112455 (2023) - [j46]Apoorv Srivastava
Feature-informed data assimilation. J. Comput. Phys. 494: 112499 (2023) - [i19]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 abs/2302.05313 (2023) - [i18]Hannah Lu, Daniel M. Tartakovsky:
Learning Nonautonomous Systems via Dynamic Mode Decomposition. CoRR abs/2306.15618 (2023) - [i17]Taniya Kapoor
Neural oscillators for generalization of physics-informed machine learning. CoRR abs/2308.08989 (2023) - [i16]Abhishek Chandra, Taniya Kapoor
Neural oscillators for magnetic hysteresis modeling. CoRR abs/2308.12002 (2023) - [i15]Hongli Zhao, Daniel M. Tartakovsky:
Physics-Aware Reduced-Order Modeling of Nonautonomous Advection-Dominated Problems. CoRR abs/2309.05117 (2023) - 2022
- [j45]Francesca Boso
Information geometry of physics-informed statistical manifolds and its use in data assimilation. J. Comput. Phys. 467: 111438 (2022) - [j44]Wenjie Shi
Polynomial Chaos Expansions for Stiff Random ODEs. SIAM J. Sci. Comput. 44(3): 1021- (2022) - [c5]Abhishek Chandra
Physics-informed neural networks for modelling anisotropic and bi-anisotropic electromagnetic constitutive laws through indirect data. SSCI 2022: 1451-1459 - [i14]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
Machine Learning in Heterogeneous Porous Materials. CoRR abs/2202.04137 (2022) - [i13]Hannah Lu, Daniel M. Tartakovsky:
Model Reduction via Dynamic Mode Decomposition. CoRR abs/2204.09590 (2022) - [i12]Wei Kang, Daniel M. Tartakovsky, Apoorv Srivastava:
Feature-Informed Data Assimilation - Definitions and Illustrative Examples. CoRR abs/2211.00256 (2022) - 2021
- [j43]Eric Joseph Hall
GINNs: Graph-Informed Neural Networks for multiscale physics. J. Comput. Phys. 433: 110192 (2021) - [j42]Joseph Bakarji, Daniel M. Tartakovsky
Data-driven discovery of coarse-grained equations. J. Comput. Phys. 434: 110219 (2021) - [j41]Hannah Lu, Daniel M. Tartakovsky
Extended dynamic mode decomposition for inhomogeneous problems. J. Comput. Phys. 444: 110550 (2021) - [j40]Søren Taverniers, Eric Joseph Hall
Mutual information for explainable deep learning of multiscale systems. J. Comput. Phys. 444: 110551 (2021) - [j39]Francesca Boso
Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs. SIAM J. Sci. Comput. 43(3): A2102-A2129 (2021) - [c4]Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky:
Graph-Informed Neural Networks. AAAI Spring Symposium: MLPS 2021 - [i11]Dong H. Song, Daniel M. Tartakovsky:
Transfer Learning on Multi-Fidelity Data. CoRR abs/2105.00856 (2021) - [i10]Zitong Zhou, Nicholas Zabaras, Daniel M. Tartakovsky:
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties. CoRR abs/2110.12367 (2021) - 2020
- [j38]Chunsong Kwon, Daniel M. Tartakovsky
Modified immersed boundary method for flows over randomly rough surfaces. J. Comput. Phys. 406: 109195 (2020) - [j37]Hannah Lu, Daniel M. Tartakovsky
Lagrangian dynamic mode decomposition for construction of reduced-order models of advection-dominated phenomena. J. Comput. Phys. 407: 109229 (2020) - [j36]Søren Taverniers, Daniel M. Tartakovsky
Estimation of distributions via multilevel Monte Carlo with stratified sampling. J. Comput. Phys. 419: 109572 (2020) - [j35]Arnout M. P. Boelens
Tensor methods for the Boltzmann-BGK equation. J. Comput. Phys. 421: 109744 (2020) - [j34]Lun Yang, Peng Wang
Resource-Constrained Model Selection for Uncertainty Propagation and Data Assimilation. SIAM/ASA J. Uncertain. Quantification 8(3): 1118-1138 (2020) - [j33]Francesca Boso
Data-Informed Method of Distributions for Hyperbolic Conservation Laws. SIAM J. Sci. Comput. 42(1): A559-A583 (2020) - [j32]Hannah Lu, Daniel M. Tartakovsky
Prediction Accuracy of Dynamic Mode Decomposition. SIAM J. Sci. Comput. 42(3): A1639-A1662 (2020) - [i9]Joseph Bakarji, Daniel M. Tartakovsky:
Data-Driven Discovery of Coarse-Grained Equations. CoRR abs/2002.00790 (2020) - [i8]Zitong Zhou, Daniel M. Tartakovsky:
Markov Chain Monte Carlo with Neural Network Surrogates: Application to Contaminant Source Identification. CoRR abs/2003.02322 (2020) - [i7]Hannah Lu, Daniel M. Tartakovsky:
Dynamic Mode Decomposition for Construction of Reduced-Order Models of Hyperbolic Problems with Shocks. CoRR abs/2003.12165 (2020) - [i6]Hannah Lu, Daniel M. Tartakovsky:
Extended Dynamic Mode Decomposition for Inhomogeneous Problems. CoRR abs/2004.06205 (2020) - [i5]Eric Joseph Hall
GINNs: Graph-Informed Neural Networks for Multiscale Physics. CoRR abs/2006.14807 (2020) - [i4]Søren Taverniers, Eric Joseph Hall
Mutual Information for Explainable Deep Learning of Multiscale Systems. CoRR abs/2009.04570 (2020) - [i3]Tyler E. Maltba, Hongli Zhao, Daniel M. Tartakovsky:
Autonomous learning of nonlocal stochastic neuron dynamics. CoRR abs/2011.10955 (2020)
2010 – 2019
- 2019
- [j31]Kimoon Um
Causality and Bayesian Network PDEs for multiscale representations of porous media. J. Comput. Phys. 394: 658-678 (2019) - [i2]Søren Taverniers, Daniel M. Tartakovsky:
Estimation of distributions via multilevel Monte Carlo with stratified sampling. CoRR abs/1906.00126 (2019) - [i1]Hannah Lu, Daniel M. Tartakovsky:
Lagrangian Dynamic Mode Decomposition for Construction of Reduced-Order Models of Advection-Dominated Phenomena. CoRR abs/1908.03688 (2019) - 2018
- [j30]Tyler Maltba
Nonlocal PDF methods for Langevin equations with colored noise. J. Comput. Phys. 367: 87-101 (2018) - [j29]Arnout M. P. Boelens
Parallel tensor methods for high-dimensional linear PDEs. J. Comput. Phys. 375: 519-539 (2018) - 2017
- [j28]Søren Taverniers, Daniel M. Tartakovsky
A tightly-coupled domain-decomposition approach for highly nonlinear stochastic multiphysics systems. J. Comput. Phys. 330: 884-901 (2017) - [j27]Joseph Bakarji, Daniel M. Tartakovsky
On the use of reverse Brownian motion to accelerate hybrid simulations. J. Comput. Phys. 334: 68-80 (2017) - [j26]Søren Taverniers, Daniel M. Tartakovsky
Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor. J. Comput. Phys. 348: 139-150 (2017) - [j25]Behrang Asadi, Mano Ram Maurya, Daniel M. Tartakovsky
Doubly Penalized LASSO for Reconstruction of Biological Networks. Proc. IEEE 105(2): 319-329 (2017) - [c3]Ilenia Battiato
Role of glycocalyx in attenuation of shear stress on endothelial cells: From in vivo experiments to microfluidic circuits. ECCTD 2017: 1-4 - 2016
- [j24]Søren Taverniers, Alexander Y. Pigarov, Daniel M. Tartakovsky
Conservative tightly-coupled simulations of stochastic multiscale systems. J. Comput. Phys. 313: 400-414 (2016) - [j23]David A. Barajas-Solano
Stochastic Collocation Methods for Nonlinear Parabolic Equations with Random Coefficients. SIAM/ASA J. Uncertain. Quantification 4(1): 475-494 (2016) - 2015
- [j22]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: 131 (2015) - [j21]Michael Sinsbeck, Daniel M. Tartakovsky
Impact of Data Assimilation on Cost-Accuracy Tradeoff in Multifidelity Models. SIAM/ASA J. Uncertain. Quantification 3(1): 954-968 (2015) - 2014
- [j20]Farzaneh Farhangmehr, Mano Ram Maurya, Daniel M. Tartakovsky
Information theoretic approach to complex biological network reconstruction: application to cytokine release in RAW 264.7 macrophages. BMC Syst. Biol. 8: 77 (2014) - [j19]Søren Taverniers, Francis J. Alexander, Daniel M. Tartakovsky
Noise propagation in hybrid models of nonlinear systems: The Ginzburg-Landau equation. J. Comput. Phys. 262: 313-324 (2014) - 2013
- [j18]Delphine Roubinet
Particle-tracking simulations of anomalous transport in hierarchically fractured rocks. Comput. Geosci. 50: 52-58 (2013) - [j17]Daniele Venturi, Daniel M. Tartakovsky
Exact PDF equations and closure approximations for advective-reactive transport. J. Comput. Phys. 243: 323-343 (2013) - [j16]Peng Wang, Daniel M. Tartakovsky
CDF Solutions of Buckley-Leverett Equation with Uncertain Parameters. Multiscale Model. Simul. 11(1): 118-133 (2013) - [j15]Will Cousins
A New Physiological Boundary Condition for Hemodynamics. SIAM J. Appl. Math. 73(3): 1203-1223 (2013) - [c2]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: 214-217 - 2012
- [j14]Peng Wang, Daniel M. Tartakovsky
Uncertainty quantification in kinematic-wave models. J. Comput. Phys. 231(23): 7868-7880 (2012) - [c1]Behrang Asadi, Daniel M. Tartakovsky, Mano Ram Maurya, Shankar Subramaniam:
Doubly Penalized LASSO for Reconstruction of Biological Networks. HISB 2012: 129 - 2010
- [j13]Gowri Srinivasan
Random walk particle tracking simulations of non-Fickian transport in heterogeneous media. J. Comput. Phys. 229(11): 4304-4314 (2010) - [j12]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): 6995-7012 (2010)
2000 – 2009
- 2008
- [j11]Alexandre M. Tartakovsky, Daniel M. Tartakovsky
Hybrid Simulations of Reaction-Diffusion Systems in Porous Media. SIAM J. Sci. Comput. 30(6): 2799-2816 (2008) - 2007
- [j10]Daniel M. Tartakovsky, Dongbin Xiu:
Guest Editors' Introduction: Stochastic Modeling of Complex Systems. Comput. Sci. Eng. 9(2): 8-9 (2007) - 2006
- [j9]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): 166-175 (2006) - [j8]Daniel M. Tartakovsky
Stochastic analysis of transport in tubes with rough walls. J. Comput. Phys. 217(1): 248-259 (2006) - [j7]Dongbin Xiu, Daniel M. Tartakovsky
Numerical Methods for Differential Equations in Random Domains. SIAM J. Sci. Comput. 28(3): 1167-1185 (2006) - [j6]Brendt Wohlberg
Subsurface characterization with support vector machines. IEEE Trans. Geosci. Remote. Sens. 44(1): 47-57 (2006) - 2005
- [j5]Daniel M. Tartakovsky, Francis J. Alexander:
Guest Editors' Introduction: Multiphysics Modeling. Comput. Sci. Eng. 7(3): 14-15 (2005) - [j4]Francis J. Alexander, Daniel M. Tartakovsky, Alejandro L. Garcia
Noise in algorithm refinement methods. Comput. Sci. Eng. 7(3): 32-38 (2005) - 2004
- [j3]Dongbin Xiu, Daniel M. Tartakovsky
A Two-Scale Nonperturbative Approach to Uncertainty Analysis of Diffusion in Random Composites. Multiscale Model. Simul. 2(4): 662-674 (2004) - [j2]Daniel M. Tartakovsky
Effective Properties of Random Composites. SIAM J. Sci. Comput. 26(2): 625-635 (2004) - 2001
- [j1]Daniel M. Tartakovsky, C. L. Winter:
Dynamics of Free Surfaces in Random Porous Media. SIAM J. Appl. Math. 61(6): 1857-1876 (2001)
Coauthor Index
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last updated on 2024-11-25 22:48 CET by the dblp team
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