# Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly

arXiv: https://arxiv.org/abs/2011.10861 · HTML: https://mlsanity.com/paper/2011.10861/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Partitioned Active Learning for Heterogeneous Systems](https://mlsanity.com/paper/2105.08547/) (2021)
- [Failure-averse Active Learning for Physics-constrained Systems](https://mlsanity.com/paper/2110.14443/) (2021)
- [An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks](https://mlsanity.com/paper/2211.09184/) (2022)
- [Multi-output Extreme Spatial Model for Complex Aircraft Production Systems](https://mlsanity.com/paper/2604.22548/) (2026)

## Abstract (arXiv)

Developing machine learning enabled smart manufacturing is promising for composite structures assembly process. To improve production quality and efficiency of the assembly process, accurate predictive analysis on dimensional deviations and residual stress of the composite structures is required. The novel composite structures assembly involves two challenges: (i) the highly nonlinear and anisotropic properties of composite materials; and (ii) inevitable uncertainty in the assembly process. To overcome those problems, we propose a neural network Gaussian process model considering input uncertainty for composite structures assembly. Deep architecture of our model allows us to approximate a complex process better, and consideration of input uncertainty enables robust modeling with complete incorporation of the process uncertainty. Based on simulation and case study, the NNGPIU can outperform other benchmark methods when the response function is nonsmooth and nonlinear. Although we use composite structure assembly as an example, the proposed methodology can be applicable to other engineering systems with intrinsic uncertainties.