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ORCIDDavid A. Barajas-Solano
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2020 – today
- 2024
[j12]Yu-Hong Yeung, David A. Barajas-Solano
, Alexandre M. Tartakovsky:
Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems. J. Comput. Phys. 502: 112788 (2024)- [j11]Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky:
Randomized physics-informed machine learning for uncertainty quantification in high-dimensional inverse problems. J. Comput. Phys. 519: 113395 (2024) - [i13]Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky:
Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation. CoRR abs/2407.04617 (2024) - 2023
- [i12]Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky:
Gaussian process regression and conditional Karhunen-Loéve models for data assimilation in inverse problems. CoRR abs/2301.11279 (2023) - [i11]Yu-Hong Yeung, Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky:
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling. CoRR abs/2307.02572 (2023) - [i10]Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky:
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems. CoRR abs/2312.06177 (2023) - 2022
- [j10]Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz:
Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems. IEEE Access 10: 33440-33448 (2022) - [i9]Subhrajit Sinha, Sai Pushpak Nandanoori, David Barajas-Solano:
Online Real-time Learning of Dynamical Systems from Noisy Streaming Data. CoRR abs/2212.05259 (2022) - 2021
- [j9]Alexandre M. Tartakovsky, David A. Barajas-Solano, QiZhi He:
Physics-informed machine learning with conditional Karhunen-Loève expansions. J. Comput. Phys. 426: 109904 (2021) - [j8]Jacob Roth, David A. Barajas-Solano, Panos Stinis, Jonathan Weare, Mihai Anitescu:
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure. Multiscale Model. Simul. 19(1): 208-241 (2021) - [i8]Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky:
Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems. CoRR abs/2108.00037 (2021) - 2020
- [j7]Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky:
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models. J. Comput. Phys. 418: 109604 (2020) - [i7]Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz:
Dynamic mode decomposition for forecasting and analysis of power grid load data. CoRR abs/2010.04248 (2020) - [i6]Tong Ma, David Alonso Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky:
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids. CoRR abs/2010.04591 (2020)
2010 – 2019
- 2019
- [j6]David A. Barajas-Solano, Alexandre M. Tartakovsky:
Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients. J. Comput. Phys. 395: 247-262 (2019) - [j5]Xiu Yang, David A. Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky:
Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence. J. Comput. Phys. 395: 410-431 (2019) - [c1]Liu Yang, Prabhat, George E. Karniadakis, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston:
Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. DLS@SC 2019: 1-11 - [i5]Tong Ma, Renke Huang, David A. Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky:
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression. CoRR abs/1910.03783 (2019) - [i4]Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston, Prabhat, George E. Karniadakis:
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. CoRR abs/1910.13444 (2019) - [i3]QiZhi He, David Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky:
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport. CoRR abs/1912.02968 (2019) - [i2]Jacob Roth, David A. Barajas-Solano, Panagiotis Stinis, Jonathan Weare, Mihai Anitescu:
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure. CoRR abs/1912.08081 (2019) - 2018
- [j4]David A. Barajas-Solano, Alexandre M. Tartakovsky:
Probability and Cumulative Density Function Methods for the Stochastic Advection-Reaction Equation. SIAM/ASA J. Uncertain. Quantification 6(1): 180-212 (2018) - [i1]Xiu Yang, David A. Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky:
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence. CoRR abs/1811.09757 (2018) - 2016
- [j3]David A. Barajas-Solano, Daniel M. Tartakovsky:
Stochastic Collocation Methods for Nonlinear Parabolic Equations with Random Coefficients. SIAM/ASA J. Uncertain. Quantification 4(1): 475-494 (2016) - [j2]David A. Barajas-Solano, Alexandre M. Tartakovsky:
Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions. Multiscale Model. Simul. 14(4): 1341-1376 (2016) - 2015
- [j1]Peng Wang, David A. Barajas-Solano, Emil M. Constantinescu, Shrirang Abhyankar, Debojyoti Ghosh, Barry F. Smith, Zhenyu Huang, Alexandre M. Tartakovsky:
Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input. SIAM/ASA J. Uncertain. Quantification 3(1): 873-896 (2015)
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