29 citations · 29 across the 5 of their papers we have counts for
5 papers
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling
Yu-Hong Yeung, Ramakrishna Tipireddy, David A. Barajas-Solano +1
We propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems as a function of the systems' spatially heterogeneous paramet…
Gaussian process regression and conditional Karhunen-Loéve models for data assimilation in inverse problems
Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
We present a model inversion algorithm, CKLEMAP, for data assimilation and parameter estimation in partial differential equation models of physical systems with spatially heterogen…
Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems
Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
We develop a physics-informed machine learning approach for large-scale data assimilation and parameter estimation and apply it for estimating transmissivity and hydraulic head in…
AMPS: A Real-time Mesh Cutting Algorithm for Surgical Simulations
Yu-Hong Yeung, Alex Pothen, Jessica Crouch
We present the AMPS algorithm, a finite element solution method that combines principal submatrix updates and Schur complement techniques, well-suited for interactive simulations o…
AMPS: An Augmented Matrix Formulation for Principal Submatrix Updates with Application to Power Grids
Yu-Hong Yeung, Alex Pothen, Mahantesh Halappanavar +1
We present AMPS, an augmented matrix approach to update the solution to a linear system of equations when the matrix is modified by a few elements within a principal submatrix. Thi…