David A. Barajas-Solano

Orcid: 0000-0003-1442-8086

According to our database1, David A. Barajas-Solano authored at least 22 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Randomized physics-informed machine learning for uncertainty quantification in high-dimensional inverse problems.
J. Comput. Phys., 2024

Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems.
J. Comput. Phys., 2024

Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation.
CoRR, 2024

2023
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling.
CoRR, 2023

2022
Online Real-time Learning of Dynamical Systems from Noisy Streaming Data.
CoRR, 2022

Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems.
IEEE Access, 2022

2021
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure.
Multiscale Model. Simul., 2021

Physics-informed machine learning with conditional Karhunen-Loève expansions.
J. Comput. Phys., 2021

Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems.
CoRR, 2021

2020
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models.
J. Comput. Phys., 2020

Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids.
CoRR, 2020

Dynamic mode decomposition for forecasting and analysis of power grid load data.
CoRR, 2020

2019
Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence.
J. Comput. Phys., 2019

Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients.
J. Comput. Phys., 2019

Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport.
CoRR, 2019

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs.
CoRR, 2019

Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression.
CoRR, 2019

Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs.
Proceedings of the Third IEEE/ACM Workshop on Deep Learning on Supercomputers, 2019

2018
Probability and Cumulative Density Function Methods for the Stochastic Advection-Reaction Equation.
SIAM/ASA J. Uncertain. Quantification, 2018

2016
Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions.
Multiscale Model. Simul., 2016

Stochastic Collocation Methods for Nonlinear Parabolic Equations with Random Coefficients.
SIAM/ASA J. Uncertain. Quantification, 2016

2015
Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input.
SIAM/ASA J. Uncertain. Quantification, 2015


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