Summary of Multifidelity Digital Twin For Real-time Monitoring Of Structural Dynamics in Aquaculture Net Cages, by Eirini Katsidoniotaki et al.
Multifidelity digital twin for real-time monitoring of structural dynamics in aquaculture net cages
by Eirini Katsidoniotaki, Biao Su, Eleni Kelasidi, Themistoklis P. Sapsis
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel framework for integrating multifidelity surrogate models into a digital twin is proposed to monitor aquaculture net cage structural dynamics under stochastic marine conditions. The framework combines low-fidelity simulation data with high-fidelity field sensor measurements using a nonlinear autoregressive Gaussian process method, which learns complex cross-correlations between models of varying fidelity. This approach can benefit applications where application-specific data are scarce, providing rapid predictions and real-time system representation for remote operations with unmanned underwater vehicles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Aquaculture is important for food production and climate change. To manage aquaculture farms better, we need new technologies. Digital twins can help, but they’re not widely used yet. Aquaculture net cages are critical, but they can get damaged by the sea. We want to predict how much stress these cages are under so we can prevent damage and keep fish safe. Our digital twin uses computer simulations and real data from sensors to do this. It works well and can even help us control robots underwater. |
Keywords
* Artificial intelligence * Autoregressive
