Summary of A Pod-tann Approach For the Multiscale Modeling Of Materials and Macroelement Derivation in Geomechanics, by Giovanni Piunno et al.
A POD-TANN approach for the multiscale modeling of materials and macroelement derivation in geomechanics
by Giovanni Piunno, Ioannis Stefanou, Cristina Jommi
First submitted to arxiv on: 13 Aug 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 The proposed approach combines Proper Orthogonal Decomposition (POD) with Thermodynamics-based Artificial Neural Networks (TANN) to model complex inelastic systems. By leveraging POD’s ability to reduce dimensionality, the method aims to capture the macroscopic behavior of such systems and derive macroelements for geomechanics applications. This paper presents a novel framework that integrates TANN’s thermodynamic principles with POD’s dimensionality reduction capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new approach helps us understand how big complex systems behave by breaking them down into smaller pieces. It uses special math tools called Thermodynamics-based Artificial Neural Networks (TANN) and Proper Orthogonal Decomposition (POD). By combining these tools, scientists can better study and predict the behavior of large inelastic systems, like those found in geomechanics. |
Keywords
» Artificial intelligence » Dimensionality reduction
