# Data-driven multi-scale modeling and robust optimization of composite structure with uncertainty quantification

arXiv: https://arxiv.org/abs/2210.09055 · HTML: https://mlsanity.com/paper/2210.09055/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life](https://mlsanity.com/paper/2301.06676/) (2023)
- [Improved generalization with deep neural operators for engineering systems: Path towards digital twin](https://mlsanity.com/paper/2301.06701/) (2023)

## Abstract (arXiv)

It is important to accurately model materials' properties at lower length scales (micro-level) while translating the effects to the components and/or system level (macro-level) can significantly reduce the amount of experimentation required to develop new technologies. Robustness analysis of fuel and structural performance for harsh environments (such as power uprated reactor systems or aerospace applications) using machine learning-based multi-scale modeling and robust optimization under uncertainties are required. The fiber and matrix material characteristics are potential sources of uncertainty at the microscale. The stacking sequence (angles of stacking and thickness of layers) of composite layers causes meso-scale uncertainties. It is also possible for macro-scale uncertainties to arise from system properties, like the load or the initial conditions. This chapter demonstrates advanced data-driven methods and outlines the specific capability that must be developed/added for the multi-scale modeling of advanced composite materials. This chapter proposes a multi-scale modeling method for composite structures based on a finite element method (FEM) simulation driven by surrogate models/emulators based on microstructurally informed meso-scale materials models to study the impact of operational parameters/uncertainties using machine learning approaches. To ensure optimal composite materials, composite properties are optimized with respect to initial materials volume fraction using data-driven numerical algorithms.