Rohit Tripathy
Cited by
Cited by
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
R Tripathy, I Bilionis
arXiv preprint arXiv:1802.00850, 2018
Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation
R Tripathy, I Bilionis, M Gonzalez
Journal of Computational Physics 321, 191-223, 2016
Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks
S Karumuri, R Tripathy, I Bilionis, J Panchal
Journal of Computational Physics 404, 109120, 2020
Deep active subspaces: A scalable method for high-dimensional uncertainty propagation
R Tripathy, I Bilionis
International Design Engineering Technical Conferences and Computers and …, 2019
Selecting deep neural networks that yield consistent attribution-based interpretations for genomics
A Majdandzic, C Rajesh, Z Tang, S Toneyan, EL Labelson, RK Tripathy, ...
Machine Learning in Computational Biology, 131-149, 2022
Designing interpretable convolution-based hybrid networks for genomics
R Ghotra, NK Lee, R Tripathy, PK Koo
BioRxiv, 2021.07. 13.452181, 2021
Exploiting marker genes for robust classification and characterization of single-cell chromatin accessibility
RK Kawaguchi, Z Tang, S Fischer, R Tripathy, PK Koo, J Gillis
BioRxiv, 2021.04. 01.438068, 2021
A Numerical Investigation on the Performance of an Earth Air Heat Exchanger System for the Indian District of Nagpur
R Tripathy, S Mishra, RT Karuppa Raj
Applied Mechanics and Materials 592, 1398-1402, 2014
Towards trustworthy explanations with gradient-based attribution methods
EL Labelson, R Tripathy, PK Koo
NeurIPS 2021 AI for Science Workshop, 2021
Surrogate Modeling for Uncertainty Quantification in systems Characterized by expensive and high-dimensional numerical simulators
RK Tripathy
Purdue University, 2020
Stochastic Multi-Objective Optimization Tool
JS Martinez, M Figura, I Bilionis, P Pandita, RK Tripathy
Design Optimization of a Stochastic Multi-Objective Problem: Gaussian Process Regressions for Objective Surrogates
JS Martinez, P Pandita, RK Tripathy, I Bilionis
Multi-objective optimization under uncertainty using the hyper-volume expected improvement
M Figura, P Pandita, RK Tripathy, I Bilionis
Efficient exploration of quantified uncertainty in granular crystals
J Lopez, R Tripathy, I Bilionis, M Gonzalez
School of Mechanical Engineering, Purdue University, 2015
Gaussian processes with built-in dimensionality reduction: Applications in high-dimensional uncertainty quantification
RK Tripathy
Purdue University, 2015
Efficient Exploration of Quantified Uncertainty in Granular Crystals
JC Lopez Ramirez, M Gonzalez, I Bilionis, RK Tripathy
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