ReviewArchives of computational methods in engineering : state of the art reviews2026
Computational Intelligence in Stochastic Reconstruction of Porous Microstructures for Image-Based Poro/Micro-Mechanical Modeling.
Review in Archives of computational methods in engineering : state of the art reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding microstructure-property relationships (MPRs) in random porous media is a fundamental challenge across numerous scientific and engineering disciplines. Image-based poro/micro-mechanical modeling offers a powerful noninvasive technique to investigate MPRs via numerical simulations. However, the stochastic nature and inherent randomness of porous media necessitate extensive datasets of 3D digital microstructures for reliable statistical analysis. Stochastic microstructure reconstruction provides an efficient and cost-effective approach to generate large numbers of virtual microstructures using limited statistical information from real porous materials, establishing it as a critical tool for advancing research in this field. This review presents a comprehensive examination of stochastic reconstruction methodologies, spanning traditional algorithm-based methods and emerging computational intelligence-based approaches. Particular emphasis is placed on computational intelligence-based approaches, such as generative adversarial networks, while also discussing the foundational contributions and limitations of traditional methods. These advancements have significantly enhanced the fidelity, efficiency and scalability of microstructure reconstruction, enabling robust statistical investigations of MPRs, such as Monte Carlo analysis. Despite substantial progress, challenges such as data scarcity, high computational costs, limited interpretability, and the need for physically realistic reconstruction remain, especially for complex pore network systems. Emerging trends, including physics-aware machine learning and hybrid AI frameworks integrating domain-specific knowledge, offer promising avenues to overcome these limitations. By bridging disciplinary gaps, this review provides a roadmap for future research in stochastic microstructure reconstruction, facilitating deeper insights into MPRs and broadening applications across various scientific and engineering domains.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.