Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks
2026-05-04 • Machine Learning
Machine Learning
AI summaryⓘ
The authors review how multi-fidelity surrogate models help predict the behavior of complex composite materials without expensive experiments or simulations. These models combine lots of low-cost, less accurate data with limited high-accuracy data to make reliable predictions. They explain different methods like Gaussian processes and neural networks, showing how these approaches differ in handling uncertainty and data from different sources. The review also discusses practical examples and the challenges specific to composites, such as varying accuracy depending on damage or manufacturing history.
Composite materialsMulti-fidelity modelingGaussian processCo-KrigingSurrogate modelingUncertainty quantificationInverse optimizationHierarchical propertiesMaterial designNonlinear damage
Authors
Haizhou Wen, Elham Kiyani, Gang Li, Srikanth Pilla, George Em Karniadakis, Zhen Li
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.