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Fundamental and Computational Sciences Directorate

Staff information

David Barajas-Solano

Computational Math Group

PNNL Publications


  • Dylewsky D., D.A. Barajas-Solano, T. Ma, A.M. Tartakovsky, and J.N. Kutz. 2022. "Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems." IEEE Access 10. PNNL-SA-167109. doi:10.1109/ACCESS.2022.3161438
  • Hirsh S.M., D.A. Barajas-Solano, and N. Kutz. 2022. "Sparsifying Priors for Bayesian Uncertainty Quantification in Model Discovery." Royal Society Open Science 9, no. 2:Art. No. 211823. PNNL-SA-160103. doi:10.1098/rsos.211823


  • Roth J., D.A. Barajas-Solano, P. Stinis, J.Q. Weare, and M. Anitescu. 2021. "A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure." Multiscale Modeling & Simulation 19, no. 1:208-241. PNNL-SA-150017. doi:10.1137/19M1306865
  • Tartakovsky A.M., D.A. Barajas-Solano, and Q. He. 2021. "Physics-Informed Machine Learning with Conditional Karhunen-Loève Expansions." Journal of Computational Physics 426. PNNL-SA-149882. doi:10.1016/


  • He Q., D.A. Barajas-Solano, G.D. Tartakovsky, and A.M. Tartakovsky. 2020. "Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport." Advances in Water Resources 141. PNNL-SA-149626. doi:10.1016/j.advwatres.2020.103610
  • Tartakovsky A.M., and D.A. Barajas-Solano. 2020. "Explaining persistent incomplete mixing in multicomponent reactive transport with Eulerian stochastic model." Advances in Water Resources 145. PNNL-SA-160657. doi:10.1016/j.advwatres.2020.103729
  • Tartakovsky A.M., C.M. Ortiz Marrero, P. Perdikaris, G.D. Tartakovsky, and D.A. Barajas-Solano. 2020. "Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems." Water Resources Research 56, no. 5:Article No. e2019WR026731. PNNL-SA-137164. doi:10.1029/2019WR026731


  • Barajas-Solano D.A., and A.M. Tartakovsky. 2019. "Approximate Bayesian Model Inversion for PDEs with Heterogeneous and State-Dependent Coefficients." Journal of Computational Physics 395. PNNL-SA-141387. doi:10.1016/
  • Barajas-Solano D.A., and Z. Huang. 2019. "Stochastic Resonance When Uncertainty Meets Dynamics." Notices of the American Mathematical Society 66, no. 1:106-107. PNNL-SA-147848. doi:10.1090/noti1766
  • Barajas-Solano D.A., F.J. Alexander, M. Anghel, and D.M. Tartakovsky. 2019. "Efficient gHMC Reconstruction of Contaminant Release History." Frontiers in Environmental Science 7. PNNL-SA-147257. doi:10.3389/fenvs.2019.00149
  • Yang L., S. Treichler, T. Kurth, K. Fischer, D.A. Barajas-Solano, J. Romero, and V. Churavy, et al. 2019. "Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs." In IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS 2019), Held in Conjunction with The International Conference for High Performance Computing, Networking, Storage and Analysis (SC2019), November 17-22, 2019, Denver, CO. Los Alamitos, California:IEEE Computer Society. PNNL-SA-142756. doi:10.1109/DLS49591.2019.00006
  • Yang X., D.A. Barajas-Solano, G.D. Tartakovsky, and A.M. Tartakovsky. 2019. "Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence." Journal of Computational Physics 395. PNNL-SA-139726. doi:10.1016/


  • Barajas-Solano D.A., and A.M. Tartakovsky. 2018. "Probability and cumulative density function methods for the stochastic advection-reaction equation." SIAM/ASA Journal on Uncertainty Quantification 6, no. 1:180-212. PNNL-SA-123069. doi:10.1137/16M1109163


  • Barajas-Solano D.A., and A.M. Tartakovsky. 2016. "Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions." Multiscale Modeling & Simulation 14, no. 4:1341-1376. PNNL-SA-110744. doi:10.1137/15M1022537
  • Barajas-Solano D.A., and A.M. Tartakovsky. 2016. "Probabilistic density function method for nonlinear dynamical systems driven by colored noise." Physical Review E 93, no. 5:Article No. 052121. PNNL-SA-114643. doi:10.1103/PhysRevE.93.052121


  • Wang P., D.A. Barajas-Solano, E. Constantinescu, S. Abhyankar, D.L. Ghosh, B. Smith, and Z. Huang, et al. 2015. "Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input." SIAM/ASA Journal on Uncertainty Quantification 3, no. 1:873-896. PNNL-SA-100191. doi:10.1137/130940050

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