Summary of A Digital Twin For Diesel Engines: Operator-infused Pinns with Transfer Learning For Engine Health Monitoring, by Kamaljyoti Nath et al.
A Digital twin for Diesel Engines: Operator-infused PINNs with Transfer Learning for Engine Health Monitoring
by Kamaljyoti Nath, Varun Kumar, Daniel J. Smith, George Em Karniadakis
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study aims to develop an efficient neural network-based approach for identifying unknown parameters of a mean value diesel engine model, enabling physics-based health monitoring and maintenance forecasting. The researchers propose a hybrid method combining physics-informed neural networks (PINNs) and deep neural operators (DeepONet) to predict unknown parameters and gas flow dynamics in a diesel engine. To reduce the computational cost of PINNs, the study proposes two transfer learning strategies: multi-stage transfer learning for parameter identification and training output weights and biases of pre-trained networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers aim to improve the efficiency and accuracy of monitoring and maintenance in diesel engines by developing an innovative approach that combines physics-informed neural networks (PINNs) with deep neural operators (DeepONet). They propose a hybrid method to predict unknown parameters and gas flow dynamics, making it possible to monitor engine health and performance. The study also evaluates uncertainty using dropout and Gaussian noise. |
Keywords
» Artificial intelligence » Dropout » Neural network » Transfer learning
