David A. Barajas-Solano supports the Physical and Computational Sciences Directorate at Pacific Northwest National Laboratory (PNNL) and is a principal investigator (PI) and co-PI on multiple United States Department of Energy Office of Science Advanced Scientific Computing Research projects. He is currently the PNNL PI for the Mathematical Multifaceted Integrated Capabilities Center's Multifaceted Mathematics for Rare, High-Impact Events in Complex Energy and Environment System (MACSER) project, managing research activities at PNNL in coordination with all other laboratory and university partners of MACSER.

Barajas-Solano is a member of the American Geophysical Union (AGU) and the Society for Industrial and Applied Mathematics (SIAM). He has also reviewed articles for the SIAM Journal on Scientific Computing, Journal of Computational Physics, and Water Resources Research, among others.

Research Interest

  • Uncertainty quantification and Bayesian inverse modeling
  • Stochastic partial differential equations
  • Groundwater flow and transport


  • PhD in engineering science, University of California San Diego 
  • MS in mechanical and aerospace engineering, University of California San Diego
  • BS in civil engineering, Industrial University of Santander 

Affiliations and Professional Service

  • Member of AGU and SIAM

Awards and Recognitions

  • Young Talents lecturer, Civil Engineering, Industrial University of Santander (2008)



  • 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