| Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification R Tripathy, I Bilionis arXiv preprint arXiv:1802.00850, 2018 | 447 | 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 | 193 | 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 | 144 | 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 | 15 | 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 | 4 | 2022 |
| Designing interpretable convolution-based hybrid networks for genomics R Ghotra, NK Lee, R Tripathy, PK Koo BioRxiv, 2021.07. 13.452181, 2021 | 4 | 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 | 4* | 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 | 3 | 2014 |
| Towards trustworthy explanations with gradient-based attribution methods EL Labelson, R Tripathy, PK Koo NeurIPS 2021 AI for Science Workshop, 2021 | 1 | 2021 |
| Surrogate Modeling for Uncertainty Quantification in systems Characterized by expensive and high-dimensional numerical simulators RK Tripathy Purdue University, 2020 | | 2020 |
| Stochastic Multi-Objective Optimization Tool JS Martinez, M Figura, I Bilionis, P Pandita, RK Tripathy | | 2016 |
| Design Optimization of a Stochastic Multi-Objective Problem: Gaussian Process Regressions for Objective Surrogates JS Martinez, P Pandita, RK Tripathy, I Bilionis | | 2016 |
| Multi-objective optimization under uncertainty using the hyper-volume expected improvement M Figura, P Pandita, RK Tripathy, I Bilionis | | 2016 |
| Efficient exploration of quantified uncertainty in granular crystals J Lopez, R Tripathy, I Bilionis, M Gonzalez School of Mechanical Engineering, Purdue University, 2015 | | 2015 |
| Gaussian processes with built-in dimensionality reduction: Applications in high-dimensional uncertainty quantification RK Tripathy Purdue University, 2015 | | 2015 |
| Efficient Exploration of Quantified Uncertainty in Granular Crystals JC Lopez Ramirez, M Gonzalez, I Bilionis, RK Tripathy | | 2015 |
Ilias BilionisAssociate Professor, Mechanical Engineering, Purdue UniversityVerified email at purdue.edu
