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2. Investigation of Compressor Cascade Flow Using Physics- Informed Neural Networks with Adaptive Learning Strategy

Author

Li, Zhihui, Montomoli, Francesco, and Sharma, Sanjiv

Subjects
Computer Science - Machine Learning, Physics - Computational Physics, Physics - Fluid Dynamics
Abstract

In this study, we utilize the emerging Physics Informed Neural Networks (PINNs) approach for the first time to predict the flow field of a compressor cascade. Different from conventional training methods, a new adaptive learning strategy that mitigates gradient imbalance through incorporating adaptive weights in conjunction with dynamically adjusting learning rate is used during the training process to improve the convergence of PINNs. The performance of PINNs is assessed here by solving both the forward and inverse problems. In the forward problem, by encapsulating the physical relations among relevant variables, PINNs demonstrate their effectiveness in accurately forecasting the compressor's flow field. PINNs also show obvious advantages over the traditional CFD approaches, particularly in scenarios lacking complete boundary conditions, as is often the case in inverse engineering problems. PINNs successfully reconstruct the flow field of the compressor cascade solely based on partial velocity vectors and near-wall pressure information. Furthermore, PINNs show robust performance in the environment of various levels of aleatory uncertainties stemming from labeled data. This research provides evidence that PINNs can offer turbomachinery designers an additional and promising option alongside the current dominant CFD methods.

Published
2023
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8. Comparative Analysis of Deep Learning Approaches for Aspect-Based Sentiment Classification

Author

Satyarthi, Shailendra, Sharma, Sanjiv, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Agrawal, Jitendra, editor, Shukla, Rajesh K., editor, Sharma, Sanjeev, editor, and Shieh, Chin-Shiuh, editor

Published
2024
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9. Data Mining Techniques: A Survey and Comparative Analysis in Vehicular Ad Hoc Networks

Author

Mishra, Deepak Kumar, Sharma, Kapil, Sharma, Sanjiv, Singhal, Abhishek, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Joshi, Amit, editor, Mahmud, Mufti, editor, Ragel, Roshan G., editor, and Kartik, S., editor

Published
2024
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10. Predictive Deep Learning: An Analysis of Inception V3, VGG16, and VGG19 Models for Breast Cancer Detection

Author

Kansal, Kanika, Sharma, Sanjiv, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Rodrigues, Joel J. P. C., editor, Gupta, Suneet Kumar, editor, Cheng, Xiaochun, editor, Sarao, Pushpender, editor, and Patel, Govind Singh, editor

Published
2024
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11. A Predictive Deep Learning Ensemble-Based Approach for Advanced Cancer Classification

Author

Kansal, Kanika, Sharma, Sanjiv, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Rodrigues, Joel J. P. C., editor, Gupta, Suneet Kumar, editor, Cheng, Xiaochun, editor, Sarao, Pushpender, editor, and Patel, Govind Singh, editor

Published
2024
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43. Trust Prediction Using Ant Colony Optimization and Particle Swarm Optimization in Social Networks

Author

Goyal, Rajeev, Updhyay, Arvind K., Sharma, Sanjiv, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Rathore, Vijay Singh, editor, Worring, Marcel, editor, Mishra, Durgesh Kumar, editor, Joshi, Amit, editor, and Maheshwari, Shikha, editor

Published
2019
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47. An efficient approach to detect conceptual level plagiarism using text mining.

Author

Mishra, Devendra Kumar, Upadhyay, Arvind Kumar, and Sharma, Sanjiv

Abstract

Plagiarism is defined as expressing thoughts or ideas of someone as their own original work. Plagiarism detection systems can improve education quality by assisting teachers in detecting plagiarism. The common technique for a plagiarism detection approach uses a variety of specific metrics to determine incidences of plagiarism. Almost all the plagiarism detection tools are working on the keywords or string matching concepts. The problem exists in such type of concept to detect the plagiarism is that, someone can easily rewrite or reframe the original ideas or thoughts of someone by replacing words or by changing the form of sentences without altering the sense of sentences, So the system those have key idea to detect the plagiarism by matching the keywords available in the sentences or documents not work correctly sometime. This paper presents a method to detect plagiarism on conceptual level by using the concept of text mining. It also focuses on different challenges and issues present in conceptual level plagiarism. [ABSTRACT FROM AUTHOR]

Published
2024
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