Summary of A Thermodynamically Consistent Physics-informed Deep Learning Material Model For Short Fiber/polymer Nanocomposites, by Betim Bahtiri et al.
A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites
by Betim Bahtiri, Behrouz Arash, Sven Scheffler, Maximilian Jux, Raimund Rolfes
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Numerical Analysis (math.NA)
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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 physics-informed deep learning (PIDL) model combines a long short-term memory network with a feed-forward neural network to predict internal variables required for characterizing the viscoelastic-viscoplastic behavior of short fiber-reinforced nanoparticle-filled epoxies. The model is trained to enforce thermodynamic principles, leading to a thermodynamically consistent constitutive model. By integrating the PIDL approach with cyclic loading-unloading experimental tests, the researchers demonstrate its ability to accurately predict the mechanical behavior of epoxy-based nanocomposites for different volume fractions of fibers and nanoparticles under various hygrothermal conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand how materials behave when they’re stretched or compressed. It uses deep learning to create a model that can predict how these materials will respond in different situations, like when they’re exposed to heat or moisture. The model is trained on data from experiments and helps us better understand the properties of these materials. This is important because it could help us design new materials that are stronger, lighter, and more durable. |
Keywords
* Artificial intelligence * Deep learning * Neural network
