# A Data-Driven Approach to Morphogenesis under Structural Instability

arXiv: https://arxiv.org/abs/2308.11846 · HTML: https://mlsanity.com/paper/2308.11846/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 63 references that are in the map.
- First posted on arXiv 23 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Discovering and forecasting extreme events via active learning in neural operators](https://mlsanity.com/paper/2204.02488/) (2022)

## Built on by (most cited first, 1 of 1)

- [Predicting Brain Morphogenesis via Physics-Transfer Learning](https://mlsanity.com/paper/2509.05305/) (2025)

## Abstract (arXiv)

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.