David A. Barajas-Solano

Mathematician at the Computational Mathematics group, Pacific Northwest National Laboratory (PNNL)

orcid.org/0000-0003-1442-8086, github.com/dbarajassolano, PNNL profile

Contact

PO Box 999, MSIN: K7-90, Richland, WA 99352, (509) 375-3783, David.Barajas-Solano@pnnl.gov

Research interests

Journal articles and conference proceedings

Ma, T., Barajas-Solano, D. A., & Tartakovsky, A. M. (2025). Chance Constrained Load Frequency Control of Power Systems with Wind Resources, J. Frankl. Inst., 362(2), 107478.
Zong, Y., Barajas-Solano, D. A., & Tartakovsky, A. M. (2025). Randomized Physics-informed Neural Networks for Bayesian Data Assimilation, Comput. Methods Appl. Mech. Eng., 436, 117670.
Yeung, Y. H., Tipireddy, R., Barajas-Solano, D. A., & Tartakovsky, A. M. (2024). Conditional Karhunen-Loève Regression Model with Basis Adaptation for High-dimensional Problems: Uncertainty Quantification and Inverse Modeling, Comput. Methods Appl. Mech. Eng., 418(A), 116487.
Yeung, Y. H., Barajas-Solano, D. A., & Tartakovsky, A. M. (2024). Gaussian Process Regression and Conditional Karhunen-Loève Models for Data Assimilation in Inverse Problems, J. Comput. Phys., 502, 112788.
Zong, Y., Barajas-Solano, D. A., & Tartakovsky, A. M. (2024). Randomized Physics-informed Machine Learning for Uncertainty Quantification in High-dimensional Inverse Problems, J. Comput. Phys., 519, 113395.
Sinha, S., Nandanoori, S. P., & Barajas-Solano, D. A. (2023). Online Real-time Learning of Dynamical Systems from Noisy Streaming Data, Sci. Rep., 13, 22564.
Tartakovsky, A. M., Ma, T., Barajas-Solano, D. A., & Tipireddy, R. (2023). Physics-informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids, Int. J. Forecasting, 39(2), 967–980.
Ma, T., Huang, R., Barajas-Solano, D. A., Tipireddy, R., & Tartakovsky, A. M. (2022). Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression, J. Mach. Learn. Mod. Comput., 3(2), 87–110.
Yeung, Y. H., Barajas-Solano, D. A., & Tartakovsky, A. M. (2022). Physics-informed Machine Learning Method for Large-scale Data Assimilation Problems, Water Resour. Res., 58(5), e2021WR031023.
Hirsh, S. M., Barajas-Solano, D. A., & Kutz, J. N. (2022). Sparsifying Priors for Bayesian Uncertainty Quantification in Model Discovery, Roy. Soc. Open. Sci., 9(2), 211823.
Dylewsky, D., Barajas-Solano, D. A., Ma, T., Tartakovsky, A. M., & Kutz, J. N. (2022). Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-periodic Systems, IEEE Access, 10, 33440–33448.
Roth, J., Barajas-Solano, D. A., Stinis, P., Weare, J., & Anitescu, M. (2021). A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure, Multiscale Model. Simul., 19(1), 208–241.
Tartakovsky, A. M., Barajas-Solano, D. A., & He, Q. (2021). Physics-Informed Machine Learning with Conditional Karhunen-Loève Expansions, J. Comput. Phys., 426, 109904.
Tipireddy, R., Barajas-Solano, D. A., & Tartakovsky, A. M. (2020). Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models, J. Comput. Phys., 418, 109604.
Tartakovsky, A. M., & Barajas-Solano, D. A. (2020). Explaining Persistent Incomplete Mixing in Multicomponent Reactive Transport with Eulerian Stochastic Model, Adv. Water Resour., 145, 103729.
Tartakovsky, A. M., Perdikaris, P., Ortiz Marrero, C., Tartakovsky, G. D., & Barajas-Solano, D. A. (2020). Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems, Water Resour. Res., 56, e2019WR026731.
He, Q., Barajas-Solano, D. A., Tartakovsky, G. D., & Tartakovsky, A. M. (2020). Physics-informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport, Adv. Water Resour., 141, 103610.
Barajas-Solano, D. A., & Tartakovsky, A. M. (2019). Approximate Bayesian Model Inversion for PDEs with Heterogeneous and State-dependent Coefficients, J. Comput. Phys., 395, 247-262.
Barajas-Solano, David A., Alexander, F. J., Anghel, M., & Tartakovsky, D. M. (2019). Efficient gHMC Reconstruction of Contaminant Release History, Front. Environ. Sci., 7.
Yang, L., Treichler, S., Kurth, T., Fischer, K., Barajas-Solano, D. A., Romero, J., Churavy, V., Tartakovsky, A. M., Houston, M., Prabhat, & Karniadakis, G. E. (2019). Highly-scalable, Physics-informed GANs for Learning Solutions of Stochastic PDEs, 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS), 1–11.
Yang, X., Barajas-Solano, D. A., Tartakovsky, G., & Tartakovsky, A. M. (2019). Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence, J. Comput. Phys., 395, 410-431.
Barajas-Solano, D. A., & Tartakovsky, A. M. (2018). Probability and Cumulative Density Function Methods for the Stochastic Advection-Reaction Equation, SIAM/ASA J. Uncert. Quantif., 6(1), 180-212.
Barajas-Solano, D. A., & Tartakovsky, A. M. (2016). Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions, Multiscale Model. Simul., 14(4), 1341-1376.
Barajas-Solano, D. A., & Tartakovsky, A. M. (2016). Probabilistic Density Function Method for Nonlinear Dynamical Systems Driven by Colored Noise, Phys. Rev. E, 93(5), 052121.
Barajas-Solano, D. A., & Tartakovsky, D. M. (2016). Stochastic Collocation Methods for Nonlinear Parabolic Equations with Random Coefficients, SIAM/ASA J. Uncert. Quantif., 4(1), 475-494.
Wang, P., Barajas-Solano, D. A., Constantinescu, E., Abhyankar, S., Ghosh, D., Smith, B. F., Huang, Z., & Tartakovsky, D. M. (2015). Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input, SIAM/ASA J. Uncert. Quantif., 3(1), 873-896.
Barajas-Solano, D. A., Wohlberg, B. E., Vesselinov, V. V., & Tartakovsky, D. M. (2014). Linear Functional Minimization for Inverse Modeling, Water Resour. Res., 51(6), 4516-4531.
Barajas-Solano, D. A., & Tartakovsky, D. M. (2013). Computing Green’s Functions for Flow in Heterogeneous Composite Media, Int. J. Uncertain. Quantif., 3(1), 39-46.

arXiv preprints

Zeng, J., Wang, Y., Tartakovsky, A. M., & Barajas-Solano, D. A. (2024). Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data, arXiv preprint arXiv:2412.04565.
Venkatasubramanian, S., & Barajas-Solano, D. A. (2024). Variational Encoder-Decoders for Learning Latent Representations of Physical Systems, arXiv preprint arXiv:2412.05175.
Barajas-Solano, D. A., & Tartakovsky, A. M. (2018). Multivariate Gaussian Process Regression for Multiscale Data Assimilation and Uncertainty Reduction, arXiv preprint arXiv:1804.06490.
Yang, X., Barajas-Solano, D. A., Rosenthal, W. S., & Tartakovsky, A. M. (2017). PDF Estimation for Power Grid Systems via Sparse Regression, arXiv preprint arXiv:1708.08378.