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Barajas-Solano, David A.
262  Ergebnisse:
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1

Gaussian process regression and conditional Karhunen-Loève ..:

Yeung, Yu-Hong ; Barajas-Solano, David A. ; Tartakovsky, Alexandre M.
Journal of Computational Physics.  502 (2024)  - p. 112788 , 2024
Link: https://doi.org/10.1016/..
 
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2

Conditional Karhunen–Loève regression model with Basis Adap..:

Yeung, Yu-Hong ; Tipireddy, Ramakrishna ; Barajas-Solano, David A..
Computer Methods in Applied Mechanics and Engineering.  418 (2024)  - p. 116487 , 2024
Link: https://doi.org/10.1016/..
 
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3

Physics-informed Gaussian process regression for states est..:

Tartakovsky, Alexandre M. ; Ma, Tong ; Barajas-Solano, David A..
International Journal of Forecasting.  39 (2023)  2 - p. 967-980 , 2023
Link: https://doi.org/10.1016/..
 
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4

Stochastically Forced Ensemble Dynamic Mode Decomposition f..:

Dylewsky, Daniel ; Barajas-Solano, David ; Ma, Tong..
IEEE Access.  10 (2022)  - p. 33440-33448 , 2022
Link: https://doi.org/10.1109/..
 
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5

A Kinetic Monte Carlo Approach for Simulating Cascading Tra..:

Roth, Jacob ; Barajas-Solano, David A. ; Stinis, Panos..
Multiscale Modeling & Simulation.  19 (2021)  1 - p. 208-241 , 2021
Link: https://doi.org/10.1137/..
 
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6

Conditional Karhunen-Loève expansion for uncertainty quanti..:

Tipireddy, Ramakrishna ; Barajas-Solano, David A. ; Tartakovsky, Alexandre M.
Journal of Computational Physics.  418 (2020)  - p. 109604 , 2020
Link: https://doi.org/10.1016/..
 
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7

Physics-informed neural networks for multiphysics data assi..:

He, QiZhi ; Barajas-Solano, David ; Tartakovsky, Guzel.
Advances in Water Resources.  141 (2020)  - p. 103610 , 2020
Link: https://doi.org/10.1016/..
 
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8

Explaining persistent incomplete mixing in multicomponent r..:

Tartakovsky, Alexandre M. ; Barajas-Solano, David
Advances in Water Resources.  145 (2020)  - p. 103729 , 2020
Link: https://doi.org/10.1016/..
 
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9

Physics-informed CoKriging: A Gaussian-process-regression-b..:

Yang, Xiu ; Barajas-Solano, David ; Tartakovsky, Guzel.
Journal of Computational Physics.  395 (2019)  - p. 410-431 , 2019
Link: https://doi.org/10.1016/..
 
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10

Highly-Ccalable, Physics-Informed GANs for Learning Solutio..:

, In: 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS),
Yang, Liu ; Prabhat, Mr ; Karniadakis, George... - p. 1-11 , 2019
Link: https://doi.org/10.1109/..
 
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11

Efficient gHMC Reconstruction of Contaminant Release Histor:

Barajas-Solano, David A. ; Alexander, Francis J. ; Anghel, Marian.
Frontiers in Environmental Science.  7 (2019)  - p. , 2019
Link: https://doi.org/10.3389/..
 
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12

Probability and Cumulative Density Function Methods for the..:

Barajas-Solano, David A. ; Tartakovsky, Alexandre M.
SIAM/ASA Journal on Uncertainty Quantification.  6 (2018)  1 - p. 180-212 , 2018
Link: https://doi.org/10.1137/..
 
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13

Hybrid Multiscale Finite Volume Method for Advection-Diffus..:

Barajas-Solano, David A. ; Tartakovsky, A. M.
Multiscale Modeling & Simulation.  14 (2016)  4 - p. 1341-1376 , 2016
Link: https://doi.org/10.1137/..
 
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14

Probabilistic density function method for nonlinear dynamic..:

Barajas-Solano, David A. ; Tartakovsky, Alexandre M.
Physical Review E.  93 (2016)  5 - p. , 2016
Link: https://doi.org/10.1103/..
 
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15

Stochastic Collocation Methods for Nonlinear Parabolic Equa..:

Barajas-Solano, David A. ; Tartakovsky, Daniel M.
SIAM/ASA Journal on Uncertainty Quantification.  4 (2016)  1 - p. 475-494 , 2016
Link: https://doi.org/10.1137/..
 
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