A data-driven approach to morphogenesis under structural instability

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 general 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 that are then used to recognize the abnormalities, predict their development, and assist in risk assessment and prognosis. The capabilities of identifying the key bifurcation characteristics and predicting the history-dependent development from the global and local features are demonstrated by examples of brain growth and aerospace structural design that share similar spatiotemporal features. The results of prediction and related discussion offer guidelines for disease diagnosis/prognosis and instability-tolerant design.