# HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation

arXiv: https://arxiv.org/abs/2306.08783 · HTML: https://mlsanity.com/paper/2306.08783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 58 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 21 of 21)

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- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
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- [Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling](https://mlsanity.com/paper/1710.11431/) (2017)
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- [Machine Learning in Magnetic Resonance Imaging: Image Reconstruction](https://mlsanity.com/paper/2012.05303/) (2020)
- [Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks](https://mlsanity.com/paper/1810.06118/) (2018)
- [Fast Modeling and Understanding Fluid Dynamics Systems with Encoder-Decoder Networks](https://mlsanity.com/paper/2006.05409/) (2020)
- [Connect the Dots: In Situ 4D Seismic Monitoring of CO2 Storage with Spatio-temporal CNNs](https://mlsanity.com/paper/2105.11622/) (2021)

## Abstract (arXiv)

Hybrid Optimization Software Suite (HOSS), which is a combined finite-discrete element method (FDEM), is one of the advanced approaches to simulating high-fidelity fracture and fragmentation processes but the application of pure HOSS simulation is computationally expensive. At the same time, machine learning methods, shown tremendous success in several scientific problems, are increasingly being considered promising alternatives to physics-based models in the scientific domains. Thus, our goal in this work is to build a new data-driven methodology to reconstruct the crack fracture accurately in the spatial and temporal fields. We leverage physical constraints to regularize the fracture propagation in the long-term reconstruction. In addition, we introduce perceptual loss and several extra pure machine learning optimization approaches to improve the reconstruction performance of fracture data further. We demonstrate the effectiveness of our proposed method through both extrapolation and interpolation experiments. The results confirm that our proposed method can reconstruct high-fidelity fracture data over space and time in terms of pixel-wise reconstruction error and structural similarity. Visual comparisons also show promising results in long-term