Xianrui Lyu, Xiaodan Ren^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074388 - 29 January 2026

Abstract Inverse design of advanced materials represents a pivotal challenge in materials science. Leveraging the latent space of Variational Autoencoders (VAEs) for material optimization has emerged as a significant advancement in the field of material inverse design. However, VAEs are inherently prone to generating blurred images, posing challenges for precise inverse design and microstructure manufacturing. While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent, it simultaneously imposes a substantial burden on target optimization due to an excessively high search space. To address these limitations, this study adopts a Variational… More >

Nguyen Dong Phuong¹, Nanthakumar Srivilliputtur Subbiah¹, Yabin Jin², Xiaoying Zhuang^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 263-303, 2025, DOI:10.32604/cmes.2025.067100 - 30 October 2025

Abstract Traditional inverse neural network (INN) approaches for inverse design typically require auxiliary feedforward networks, leading to increased computational complexity and architectural dependencies. This study introduces a standalone INN methodology that eliminates the need for feedforward networks while maintaining high reconstruction accuracy. The approach integrates Principal Component Analysis (PCA) and Partial Least Squares (PLS) for optimized feature space learning, enabling the standalone INN to effectively capture bidirectional mappings between geometric parameters and mechanical properties. Validation using established numerical datasets demonstrates that the standalone INN architecture achieves reconstruction accuracy equal or better than traditional tandem approaches while More >

Xiaolong Zhu, Feng Chen, Yuntian Chen, Wei Zhu, Xiaoxiao Han^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.012584

Abstract Human tissues and organs exhibit not only intricate anatomical architectures but also spatially heterogeneous distributions of elastic modulus—for example, between cancellous and cortical bone, across the epidermis, dermis, and subcutaneous layers, and between healthy and fibrotic liver tissues. Conventional biomaterials often fail to replicate such mechanical heterogeneity, thereby limiting their capacity to recreate biomimetic physiological microenvironments essential for applications like tissue regeneration and disease modeling. Meta-biomaterials, artificially engineered through the rational structural design of continuous materials, have emerged as a promising class of materials owing to their highly tunable mechanical and biological properties. These attributes… More >

Yingli Liu^1,2, Yuting Cui^1,2, Haihe Zhou^1,2, Sheng Lei³, Haibin Yuan³, Tao Shen^1,2,*, Jiancheng Yin^4,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1463-1492, 2025, DOI:10.32604/cmc.2025.060109 - 17 February 2025

Abstract Finding materials with specific properties is a hot topic in materials science. Traditional materials design relies on empirical and trial-and-error methods, requiring extensive experiments and time, resulting in high costs. With the development of physics, statistics, computer science, and other fields, machine learning offers opportunities for systematically discovering new materials. Especially through machine learning-based inverse design, machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties. This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials More > Graphic Abstract

Hirak Kansara¹, Mingchao Liu^2,*, Yinfeng He³, Wei Tan^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011328

Abstract Shape-morphing structures exhibit the remarkable ability to transition between different configurations, offering vast potential across numerous applications. A common example involves the transformation from a flat two-dimensional (2D) state to a desired three-dimensional (3D) form. One prevalent technique for fabricating such structures entails strategically cutting thin sheet materials (known as kirigami), which, upon the application of external mechanical forces, morph into the intended 3D shape. A method leveraging the non-linear beam equation has been proposed for inverse design, determining the optimal 2D cutting patterns necessary to achieve a symmetrical 3D shape. Central to this strategy… More >

Jiahao Li¹, Yinbo Zhu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011955

Abstract Movements actuated by the moisture in plant tissues are prevalent in nature. Different microstructures of plants determine the various patterns of moisture-actuated movements. For instance, the graded lignin fraction of Selaginella lepidophylla leads to the a graded curvature morphology, while the fiber orientation angles determine the helical chirality of chiral seed pods. Inspired by these two types of plant microstructures, a theoretical framework for a biomimetic Turing machine is constructed. Similar to the Turing machine introduced by Alan Turing in 1936, the biomimetic Turing machine has a ribbon-like bilayer structure composed of numerous units, whose More >