A Data-Driven Approach to Morphogenesis under Structural Instability
This work addresses the problem of predicting complex morphological patterns in living systems and engineering structures for applications in disease diagnosis/prognosis and instability-tolerant design, representing an incremental advancement through a hybrid data-driven and physics-based approach.
The authors tackled the problem of predicting morphological development patterns under structural instability by proposing a data-driven machine learning framework that uses simulation data to construct digital libraries of structural patterns. The approach demonstrated capabilities in identifying key bifurcation characteristics and predicting history-dependent development, with applications shown in brain growth and aerospace structural design.
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.