485 citations · 652 across the 4 of their papers we have counts for
4 papers
Deep active subspaces - a scalable method for high-dimensional uncertainty propagation
Rohit Tripathy, Ilias Bilionis
A problem of considerable importance within the field of uncertainty quantification (UQ) is the development of efficient methods for the construction of accurate surrogate models.…
Simulator-free Solution of High-Dimensional Stochastic Elliptic Partial Differential Equations using Deep Neural Networks
Sharmila Karumuri, Rohit Tripathy, Ilias Bilionis +1
Stochastic partial differential equations (SPDEs) are ubiquitous in engineering and computational sciences. The stochasticity arises as a consequence of uncertainty in input parame…
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
Rohit Tripathy, Ilias Bilionis
State-of-the-art computer codes for simulating real physical systems are often characterized by a vast number of input parameters. Performing uncertainty quantification (UQ) tasks…
Gaussian processes with built-in dimensionality reduction: Applications in high-dimensional uncertainty propagation
Ilias Bilionis, Rohit Tripathy, Marcial Gonzalez
The prohibitive cost of performing Uncertainty Quantification (UQ) tasks with a very large number of input parameters can be addressed, if the response exhibits some special struct…