Summary of Probabilistic Surrogate Model For Accelerating the Design Of Electric Vehicle Battery Enclosures For Crash Performance, by Shadab Anwar Shaikh et al.
Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance
by Shadab Anwar Shaikh, Harish Cherukuri, Kranthi Balusu, Ram Devanathan, Ayoub Soulami
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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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 probabilistic surrogate model accelerates the design of electric vehicle battery enclosures while ensuring high crash performance. By integrating finite element simulations and Gaussian Process Regression, the model predicts crash parameters with high accuracy and provides uncertainty estimates. The model was trained using data generated from thermoforming and crash simulations over various material and process parameters, demonstrating a mean absolute percentage error of 8.08% in validation tests against new simulation data. Additionally, a Monte Carlo study reveals the impact of input variability on outputs, highlighting the model’s ability to capture complex relationships within the dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to design electric vehicle battery enclosures that are safe and efficient. It uses special computer simulations and math to create a model that can predict how well different designs will perform in crashes. The model is very accurate, and it also shows us how much uncertainty there is in the predictions. This could help companies make better decisions about what materials to use and how to design their battery enclosures. |
Keywords
» Artificial intelligence » Regression
