A Data-Driven Approach to Morphogenesis under Structural Instability

TL;DR

This paper introduces a machine-learning framework that models morphogenesis under structural instability, enabling prediction and recognition of complex patterns for applications in medicine and engineering.

Contribution

It presents a novel data-driven approach combining physical modeling and machine learning to analyze and predict morphogenesis in unstable structures.

Findings

Successfully recognized abnormalities in structural patterns.

Predicted development of morphogenetic patterns in brain growth and aerospace design.

Provided guidelines for disease prognosis and instability-tolerant design.

Abstract

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.