A Data-Driven Approach to Morphogenesis under Structural Instability
Yingjie Zhao Zhiping Xu
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.