Modeling groundwater flow and reactive transport

I’m interested in developing efficient model inversion and uncertainty quantification methods for groundwater flow and reactive transport problems. I also work in developing multiscale and scientific machine learning-based models for these phenomena. Specific areas of interest include:

• Probabilistic, data-driven models of heterogeneous parameter fields.
• PDF methods for stochastic upscaling and uncertainty quantification.
• Bayesian methods for model inversion and data assimilation, including MCMC variants for high-dimensional problems, approximate Bayesian inference, likelihood-free, and others.
• Scientific machine learning methods for state estimation and parameter estimation.
• Multiscale schemes for continuum-scale groundwater flow and transport.

Relevant publications

• 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, 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.
• 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. (2018). Multivariate Gaussian Process Regression for Multiscale Data Assimilation and Uncertainty Reduction, arXiv preprint arXiv:1804.06490.
• 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., 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.