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Fundamental and Computational Sciences Directorate

Staff information

David Barajas-Solano

Multiscale Modeling and UQ

PNNL Publications


  • 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
  • Tipireddy R., D.A. Barajas-Solano, and A.M. Tartakovsky. 2020. "Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models." Journal of Computational Physics 418. PNNL-SA-142607.


  • 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, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 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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