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

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.
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

Roth, J., Barajas-Solano, D. A., Stinis, P., Weare, J., & Anitescu, M. (2019). A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure, arXiv preprint arXiv:1912.08081.
Tartakovsky, A. M., & Barajas-Solano, D. A. (2019). Physics-Informed Machine Learning with Conditional Karhunen-Loève Expansions, arXiv preprint arXiv:arXiv:1912.02248.
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, arXiv preprint arXiv:1910.13444.
Ma, T., Huang, R., Barajas-Solano, D. A., Tipireddy, R., & Tartakovsky, A. M. (2019). Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression, arXiv preprint arXiv:1910.03783.
Tipireddy, R., Barajas-Solano, D. A., & Tartakovsky, A. M. (2019). Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models, arXiv preprint arXiv:1904.08069.
Tartakovsky, A. M., Perdikaris, P., Ortiz Marrero, C., Tartakovsky, G. D., & Barajas-Solano, D. A. (2018). Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks, arXiv preprint arXiv:1808.03398.
Barajas-Solano, D. A., & Tartakovsky, A. M. (2018). Multivariate Gaussian Process Regression for Multiscale Data Assimilation and Uncertainty Reduction, arXiv preprint arXiv:1804.06490.
Tartakovsky, A. M., & Barajas-Solano, D. A. (2018). Persistent incomplete mixing in reactive flows, arXiv preprint arXiv:1803.06693.
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.