Sparsifying priors for Bayesian uncertainty quantification in model discovery von Hirsh, Seth M, Barajas-Solano, David A, Kutz, J Nathan

Volltext

Physics‐Informed Machine Learning Method for Large‐Scale Data Assimilation Problems von Yeung, Yu‐Hong, Barajas‐Solano, David A., Tartakovsky, Alexandre M.

Volltext

Data for: Explaining persistent incomplete mixing in multicomponent reactive transport with Eulerian stochastic model von Tartakovsky, Alexandre

Volltext bestellen

Data for: Explaining persistent incomplete mixing in multicomponent reactive transport with Eulerian stochastic model von Tartakovsky, Alexandre

Volltext bestellen

Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models von Tipireddy, Ramakrishna, Barajas-Solano, David A., Tartakovsky, Alexandre M.

Volltext

Physics-informed Gaussian process regression for states estimation and forecasting in power grids von Tartakovsky, Alexandre M., Ma, Tong, Barajas-Solano, David A., Tipireddy, Ramakrishna

Volltext

Efficient gHMC Reconstruction of Contaminant Release History von Barajas-Solano, David A., Alexander, Francis J., Anghel, Marian, Tartakovsky, Daniel M.

Volltext

Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport von He, QiZhi, Barajas-Solano, David, Tartakovsky, Guzel, Tartakovsky, Alexandre M.

Volltext

Probabilistic density function method for nonlinear dynamical systems driven by colored noise von Barajas-Solano, David A, Tartakovsky, Alexandre M

Volltext

Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence von Yang, Xiu, Barajas-Solano, David, Tartakovsky, Guzel, Tartakovsky, Alexandre M.

Volltext

Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients von Barajas-Solano, D.A., Tartakovsky, A.M.

Volltext

Linear functional minimization for inverse modeling von Barajas-Solano, D. A., Wohlberg, B. E., Vesselinov, V. V., Tartakovsky, D. M.

Volltext

Physics‐Informed Machine Learning Method for Large‐Scale Data Assimilation Problems von Yeung, Yu‐Hong, Barajas‐Solano, David A., Tartakovsky, Alexandre M.

Volltext

Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems von Yeung, Yu-Hong, Barajas-Solano, David A., Tartakovsky, Alexandre M.

Volltext

Conditional Karhunen–Loève regression model with Basis Adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling von Yeung, Yu-Hong, Tipireddy, Ramakrishna, Barajas-Solano, David A., Tartakovsky, Alexandre M.

Volltext

Conditional Karhunen–Loève regression model with Basis Adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling von Yeung, Yu-Hong, Tipireddy, Ramakrishna, Barajas-Solano, David A., Tartakovsky, Alexandre M.

Volltext

Physics-Informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids von Tartakovsky, Alexandre M., Ma, Tong, Barajas-Solano, David A., Tipireddy, Ramakrishna

Volltext

A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure von Roth, Jacob, Barajas-Solano, David A., Stinis, Panos, Weare, Jonathan, Anitescu, Mihai

Volltext

Stochastic Collocation Methods for Nonlinear Parabolic Equations with Random Coefficients von Barajas-Solano, David A., Tartakovsky, Daniel M.

Volltext

Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions von Barajas-Solano, David A., Tartakovsky, A. M.

Volltext