D. Barajas-Solano

发表

Linear functional minimization for inverse modeling

Daniel M. Tartakovsky, Brendt Wohlberg, David A. Barajas-Solano, 2015 .

PDF estimation for power grid systems via sparse regression

David A. Barajas-Solano, Alexandre M. Tartakovsky, Xiu Yang, 2017, 1708.08378.

Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport pdf

Guzel Tartakovsky, Alexandre M. Tartakovsky, QiZhi He, 2019, ArXiv.

COMPUTING GREEN'S FUNCTIONS FOR FLOW IN HETEROGENEOUS COMPOSITE MEDIA

D. Tartakovsky, D. Barajas-Solano, 2013 .

Physics-informed machine learning with conditional Karhunen-Loève expansions

A. Tartakovsky, Qizhi He, D. Barajas-Solano, 2019, J. Comput. Phys..

Explaining persistent incomplete mixing in multicomponent reactive transport with Eulerian stochastic model

A. Tartakovsky, D. Barajas-Solano, 2020 .

Physics‐Informed Machine Learning Method for Large‐Scale Data Assimilation Problems

A. Tartakovsky, Y. Yeung, D. Barajas-Solano, 2021, Water Resources Research.

Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport

A. Tartakovsky, Qizhi He, G. Tartakovsky, 2019, Advances in Water Resources.

Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression pdf

Renke Huang, Ramakrishna Tipireddy, David A. Barajas-Solano, 2019, ArXiv.

Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients

David A. Barajas-Solano, Alexandre M. Tartakovsky, A. Tartakovsky, 2019, J. Comput. Phys..

Efficient gHMC Reconstruction of Contaminant Release History

Daniel M. Tartakovsky, David A. Barajas-Solano, Marian Anghel, 2019, Front. Environ. Sci..

Gaussian process regression and conditional Karhunen-Loéve models for data assimilation in inverse problems

A. Tartakovsky, Y. Yeung, D. Barajas-Solano, 2023, SSRN Electronic Journal.

Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs

Prabhat, Liu Yang, Thorsten Kurth, 2019, 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS).

Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks

Paris Perdikaris, Alexandre M. Tartakovsky, David Barajas-Solano, 2018, 1808.03398.

Sparsifying priors for Bayesian uncertainty quantification in model discovery

J. Nathan Kutz, David A. Barajas-Solano, Seth M. Hirsh, 2021, Royal Society Open Science.

Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

Paris Perdikaris, Carlos Ortiz Marrero, P. Perdikaris, 2020, Water Resources Research.

Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence

David A. Barajas-Solano, Alexandre M. Tartakovsky, Xiu Yang, 2018, J. Comput. Phys..

Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input

Emil M. Constantinescu, Zhenyu Huang, Peng Wang, 2015, SIAM/ASA J. Uncertain. Quantification.

Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems

A. Tartakovsky, J. Kutz, D. Barajas-Solano, 2020, IEEE Access.

Dynamic mode decomposition for forecasting and analysis of power grid load data pdf

Daniel Dylewsky, Tong Ma, David Barajas-Solano, 2020, ArXiv.

Online Real-time Learning of Dynamical Systems from Noisy Streaming Data

S. Sinha, S. Nandanoori, D. Barajas-Solano, 2022, ArXiv.

Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models

Ramakrishna Tipireddy, Alexandre M. Tartakovsky, David A Barajas-Solano, 2019, J. Comput. Phys..

Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions

David A. Barajas-Solano, Alexandre M. Tartakovsky, A. Tartakovsky, 2015, Multiscale Model. Simul..

A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure pdf

Mihai Anitescu, David A. Barajas-Solano, Jonathan Weare, 2019, Multiscale Model. Simul..

Physics-informed Gaussian process regression for states estimation and forecasting in power grids

A. Tartakovsky, D. Barajas-Solano, Tong Ma, 2022, International Journal of Forecasting.

Effective Stochastic Model For Reactive Transport

A. Tartakovsky, D. Barajas-Solano, B. Zheng, 2017 .

Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids pdf

Ramakrishna Tipireddy, Tong Ma, David Alonso Barajas-Solano, 2020, ArXiv.