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541 results on '"Ravi, G."'

5. Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes

Author

Hauth, Jeremiah, Safta, Cosmin, Huan, Xun, Patel, Ravi G., and Jones, Reese E.

Subjects
Computer Science - Machine Learning, Mathematics - Statistics Theory, Physics - Computational Physics
Abstract

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural network models have proved to be adept at modeling processes with spatial-temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial-temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance.Projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape., Comment: 27 pages, 20 figures

Published
2024

8. Design and Analysis of Automatic Loading and Unloading of Casting After Fettling Process

Author

Chikkangoudar, R. N., Kelageri, Nagaraj K., Lingannavar, Ravi G., Patil, S. F., Borges, Allwyn, Jamadar, Maaz M., Naidu, Hrishikesh G., Tambitgar, Taukeer Ahmed, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Singh, D. K., editor, Hegde, Shriram, editor, and Mishra, Ashutosh, editor

Published
2024
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9. Eye Disease Classification Using VGG-19 Architecture

Author

Ravi, G., Prasanth, M., Rajendran, S., Celebi, Emre, Series Editor, Chen, Jingdong, Series Editor, Gopi, E. S., Series Editor, Neustein, Amy, Series Editor, Liotta, Antonio, Series Editor, Di Mauro, Mario, Series Editor, and Maheswaran, P, editor

Published
2024
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13. Error-in-variables modelling for operator learning

Author

Patel, Ravi G., Manickam, Indu, Lee, Myoungkyu, and Gulian, Mamikon

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Deep operator learning has emerged as a promising tool for reduced-order modelling and PDE model discovery. Leveraging the expressive power of deep neural networks, especially in high dimensions, such methods learn the mapping between functional state variables. While proposed methods have assumed noise only in the dependent variables, experimental and numerical data for operator learning typically exhibit noise in the independent variables as well, since both variables represent signals that are subject to measurement error. In regression on scalar data, failure to account for noisy independent variables can lead to biased parameter estimates. With noisy independent variables, linear models fitted via ordinary least squares (OLS) will show attenuation bias, wherein the slope will be underestimated. In this work, we derive an analogue of attenuation bias for linear operator regression with white noise in both the independent and dependent variables. In the nonlinear setting, we computationally demonstrate underprediction of the action of the Burgers operator in the presence of noise in the independent variable. We propose error-in-variables (EiV) models for two operator regression methods, MOR-Physics and DeepONet, and demonstrate that these new models reduce bias in the presence of noisy independent variables for a variety of operator learning problems. Considering the Burgers operator in 1D and 2D, we demonstrate that EiV operator learning robustly recovers operators in high-noise regimes that defeat OLS operator learning. We also introduce an EiV model for time-evolving PDE discovery and show that OLS and EiV perform similarly in learning the Kuramoto-Sivashinsky evolution operator from corrupted data, suggesting that the effect of bias in OLS operator learning depends on the regularity of the target operator., Comment: 23 pages, 10 figures

Published
2022

29. Implantology of Olecranon and Coronoid Fractures

Author

Dirckx, Margo, Jukes, Christopher, Phadnis, Joideep, Bharadwaj, Ravi G., Section editor, Kambhampati, Srinivas B. S., Section editor, Banerjee, Arindam, editor, Biberthaler, Peter, editor, and Shanmugasundaram, Saseendar, editor

Published
2023
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39. Correction to: First-line nivolumab plus ipilimumab in metastatic non-small cell lung cancer: 5-year outcomes in Japanese patients from CheckMate 227 Part 1

Author

Nishio, Makoto, Ohe, Yuichiro, Ikeda, Satoshi, Yokoyama, Toshihide, Hayashi, Hidetoshi, Fukuhara, Tatsuro, Sato, Yuki, Tanaka, Hiroshi, Hotta, Katsuyuki, Sugawara, Shunichi, Daga, Haruko, Okamoto, Isamu, Kasahara, Kazuo, Naito, Tateaki, Li, Li, Gupta, Ravi G., Bushong, Judith, and Mizutani, Hideaki

Published
2023
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47. Systemic and Intracranial Outcomes With First-Line Nivolumab Plus Ipilimumab in Patients With Metastatic NSCLC and Baseline Brain Metastases From CheckMate 227 Part 1

Author

Reck, Martin, Ciuleanu, Tudor-Eliade, Lee, Jong-Seok, Schenker, Michael, Zurawski, Bogdan, Kim, Sang-We, Mahave, Mauricio, Alexandru, Aurelia, Peters, Solange, Pluzanski, Adam, Caro, Reyes Bernabe, Linardou, Helena, Burgers, Jacobus A., Nishio, Makoto, Martinez-Marti, Alex, Azuma, Koichi, Axelrod, Rita, Paz-Ares, Luis G., Ramalingam, Suresh S., Borghaei, Hossein, O’Byrne, Kenneth J., Li, Li, Bushong, Judith, Gupta, Ravi G., Grootendorst, Diederik J., Eccles, Laura J., and Brahmer, Julie R.

Published
2023
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