A Data-Driven Approach to Morphogenesis under Structural Instability
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909262537555968 |
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| author | Zhao, Yingjie Xu, Zhiping |
| author_facet | Zhao, Yingjie Xu, Zhiping |
| contents | Morphological development into evolutionary patterns under structural instability is ubiquitous in living systems and often of vital importance for engineering structures. Here we propose a data-driven approach to understand and predict their spatiotemporal complexities. A machine-learning framework is proposed based on the physical modeling of morphogenesis triggered by internal or external forcing. Digital libraries of structural patterns are constructed from the simulation data, which are then used to recognize the abnormalities, predict their development, and assist in risk assessment and prognosis. The capabilities to identify the key bifurcation characteristics and predict the history-dependent development from the global and local features are demonstrated by examples of brain growth and aerospace structural design, which offer guidelines for disease diagnosis/prognosis and instability-tolerant design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_11846 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Data-Driven Approach to Morphogenesis under Structural Instability Zhao, Yingjie Xu, Zhiping Pattern Formation and Solitons Computer Vision and Pattern Recognition Quantitative Methods Machine Learning Morphological development into evolutionary patterns under structural instability is ubiquitous in living systems and often of vital importance for engineering structures. Here we propose a data-driven approach to understand and predict their spatiotemporal complexities. A machine-learning framework is proposed based on the physical modeling of morphogenesis triggered by internal or external forcing. Digital libraries of structural patterns are constructed from the simulation data, which are then used to recognize the abnormalities, predict their development, and assist in risk assessment and prognosis. The capabilities to identify the key bifurcation characteristics and predict the history-dependent development from the global and local features are demonstrated by examples of brain growth and aerospace structural design, which offer guidelines for disease diagnosis/prognosis and instability-tolerant design. |
| title | A Data-Driven Approach to Morphogenesis under Structural Instability |
| topic | Pattern Formation and Solitons Computer Vision and Pattern Recognition Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2308.11846 |