Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks

TL;DR

This review discusses multi-fidelity surrogate models, including Gaussian-process and neural network methods, for efficient and reliable prediction of composite material behavior across various engineering applications.

Contribution

It provides a structured overview of multi-fidelity modeling techniques tailored for composite mechanics, highlighting their distinctions, applications, and challenges.

Findings

Different multi-fidelity methods are compared in terms of correlation, discrepancy, and scalability.

Applications include rapid material design exploration, inverse optimization, and workflow integration.

Challenges include regime-dependent fidelity gaps and uncertainty propagation in composites.

Abstract

Composite materials exhibit strongly hierarchical and anisotropic properties governed by coupled mechanisms spanning constituents, plies, laminates, structures, and manufacturing history. This intrinsic complexity makes predictive modeling of composites expensive, because repeated experiments and high-fidelity simulations are needed to cover large design spaces of material, structure, and manufacturing. Multi-fidelity surrogate modeling addresses this challenge by combining abundant, less expensive data with limited high-accuracy data to recover reliable high-fidelity predictions. This review presents a structured overview of multi-fidelity modeling for composite mechanics, covering Gaussian-process or Kriging-based methods, including co-Kriging, coregionalization models, autoregressive formulations, nonlinear autoregressive Gaussian processes, multi-fidelity deep Gaussian processes, and multi-fidelity neural networks. Their distinctions are examined in terms of cross-fidelity correlation, discrepancy representation, uncertainty quantification, and scalability. Selected examples of their applications to composites are introduced according to the roles that multi-fidelity surrogates play in engineering problems, including forward prediction for rapid exploration of material design spaces, inverse optimization for composite parameter identification and design search under limited high-fidelity access, and workflow integration, where heterogeneous data sources, constraints, and validation requirements determine model utility. Open question discussions highlight recurring challenges specific to composites, such as regime-dependent fidelity gaps associated with nonlinear damage and manufacturing history, mismatches between simulations and experiments, and uncertainty propagation across multi-fidelity models.