David A. Barajas-Solano

20 papers Journal 19Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Statistical Learning Analysis of Physics-Informed Neural Networks. CoRR David A. Barajas-Solano
2024	J	`jnl`	Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems. J. Comput. Phys. Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
2024	J	`jnl`	Variational Encoder-Decoders for Learning Latent Representations of Physical Systems. CoRR Subashree Venkatasubramanian, David A. Barajas-Solano
2023	J	`jnl`	Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling. CoRR Yu-Hong Yeung, Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky
2023	J	`jnl`	Gaussian process regression and conditional Karhunen-Loéve models for data assimilation in inverse problems. CoRR Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
2021	J	`jnl`	A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure. Multiscale Model. Simul. Jacob Roth, David A. Barajas-Solano, Panos Stinis, Jonathan Weare, Mihai Anitescu
2021	J	`jnl`	Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems. CoRR Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
2021	J	`jnl`	Physics-informed machine learning with conditional Karhunen-Loève expansions. J. Comput. Phys. Alexandre M. Tartakovsky, David A. Barajas-Solano, Qizhi He
2020	J	`jnl`	Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models. J. Comput. Phys. Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky
2019	J	`jnl`	A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure. CoRR Jacob Roth, David A. Barajas-Solano, Panagiotis Stinis, Jonathan Weare, Mihai Anitescu
2019	J	`jnl`	Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients. J. Comput. Phys. David A. Barajas-Solano, Alexandre M. Tartakovsky
2019	J	`jnl`	Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression. CoRR Tong Ma, Renke Huang, David A. Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky
2019	—	`conf`	Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. DLS@SC Liu Yang, Prabhat, George E. Karniadakis, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston
2019	J	`jnl`	Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. CoRR Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston, Prabhat, George E. Karniadakis
2019	J	`jnl`	Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence. J. Comput. Phys. Xiu Yang, David A. Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky
2018	J	`jnl`	Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence. CoRR Xiu Yang, David A. Barajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky
2018	J	`jnl`	Probability and Cumulative Density Function Methods for the Stochastic Advection-Reaction Equation. SIAM/ASA J. Uncertain. Quantification David A. Barajas-Solano, Alexandre M. Tartakovsky
2016	J	`jnl`	Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions. Multiscale Model. Simul. David A. Barajas-Solano, Alexandre M. Tartakovsky
2016	J	`jnl`	Stochastic Collocation Methods for Nonlinear Parabolic Equations with Random Coefficients. SIAM/ASA J. Uncertain. Quantification David A. Barajas-Solano, Daniel M. Tartakovsky
2015	J	`jnl`	Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input. SIAM/ASA J. Uncertain. Quantification Peng Wang, David A. Barajas-Solano, Emil M. Constantinescu, Shrirang Abhyankar, Debojyoti Ghosh, Barry F. Smith, Zhenyu Huang, Alexandre M. Tartakovsky