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Crack Path Prediction with Operator Learning using Discrete Particle System data Generation

Created by
  • Haebom

Author

Elham Kiyani, Venkatesh Ananchaperumal, Ahmad Peyvan, Mahendaran Uchimali, Gang Li, George Em Karniadakis

Outline

This paper presents a machine learning model based on a deep operator network (DeepONet) that predicts crack propagation using constitutive law-informed particle dynamics (CPD) simulation data. Specifically, we explore two variants of the model, the vanilla DeepONet and the Fusion DeepONet, to predict time-dependent crack propagation in specimens with varying geometries (e.g., varying notch heights and varying hole radii). We evaluate model performance by studying three representative cases (e.g., varying notch heights and combinations of notch heights and hole radii) and demonstrate that Fusion DeepONet provides more accurate predictions, particularly in non-destructive cases. While prediction accuracy is somewhat lower in fracture cases, we highlight the generalizability of Fusion DeepONet to complex, geometrically diverse, and time-varying crack propagation phenomena.

Takeaways, Limitations

Takeaways:
We present the effectiveness of a crack propagation prediction model using Fusion DeepONet.
It shows superior prediction accuracy compared to existing methods, especially in non-destructive cases.
We verify the generalizability to complex geometric shapes and time-dependent crack propagation phenomena.
Combining constitutive law-informed particle dynamics (CPD) simulations with machine learning presents new possibilities for crack propagation prediction.
Limitations:
When destruction occurs, the prediction accuracy is somewhat low.
Additional validation of generalized performance for various materials and failure conditions is required.
The model may lack interpretability.
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