Advanced Displacement Magnitude Prediction In Multimaterial Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture

# Advanced Displacement Magnitude Prediction in Multi-Material Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture

**arXiv ID:** 2501.03254
**Authors:** Akshansh Mishra
**Published:** 2024-12-31T00:15:58Z
**Abstract:**
This paper proposes an innovative method for predicting deformation in architected lattice structures that combines Physics-Informed Neural Networks (PINNs) with finite element analysis. A thorough study was carried out on FCC-based lattice beams utilizing five different materials (Structural Steel, AA6061, AA7075, Ti6Al4V, and Inconel 718) under varied edge loads (1000-10000 N). The PINN model blends data-driven learning with physics-based limitations via a proprietary loss function, resulting in much higher prediction accuracy than linear regression. PINN outperforms linear regression, achieving greater R-square (0.7923 vs 0.5686) and lower error metrics (MSE: 0.00017417 vs 0.00036187). Among the materials examined, AA6061 had the highest displacement sensitivity (0.1014 mm at maximum load), while Inconel718 had better structural stability.

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This skill is generated from the arXiv paper: Advanced Displacement Magnitude Prediction in Multi-Material Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture (2501.03254).

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## References

- [arXiv:2501.03254](http://arxiv.org/abs/2501.03254v1)