Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Mechanical and Civil Engineering

First Advisor

Linxia Gu

Second Advisor

Darshan G. Pahinkar

Third Advisor

Ryan T. White

Fourth Advisor

Pengfei Dong


Machine learning, particularly deep neural networks, has demonstrated significant potential in predicting high-dimensional tasks across various domains. This work encompasses a detailed review of Generative AI in healthcare and three studies integrating machine learning with finite element analysis for predicting biomechanical behaviors and properties. Initially, we provide a comprehensive overview of Generative AI applications in healthcare, focusing on Transformers and Denoising Diffusion models and suggesting potential research avenues to address existing challenges.

Subsequently, we addressed soccer-related ocular injuries by combining finite element analysis and machine learning to predict retinal mechanics following a soccer ball hit rapidly. The prediction errors are between 3.03% and 16.40% for peak von Mises stress and maximum principal strain, respectively.

Furthermore, we developed end-to-end deep learning tools as an efficient alternative to finite element methods for predicting stress-strain fields within arterial walls, which is crucial for understanding atherosclerosis. Utilizing U-Net-based convolutional neural networks and conditional generative adversarial networks, combined with ensemble and transfer learning techniques, we developed models that accurately predicted von Mises stress and strain fields in the artery.

Lastly, we developed a finite element-based machine learning model for predicting the mechanical properties of bioglass-collagen composite hydrogels. This model effectively predicted Young’s modulus and Poisson’s ratio with 95% and 83% R-squared values.

This work aims to advance the intersection of machine learning, biomechanics, and healthcare. By developing accurate models for predicting biomechanical behavior and mechanical properties and providing a comprehensive review of Generative AI applications, we hope to contribute to addressing pervasive public health issues.