Electrical and Computer Engineering ETDs

Publication Date

Spring 4-15-2020


This dissertation proposes novel algorithms and applications and provides a real-time and easy-to-use simulator for realistic animation of the 3D solid model. The Finite Element Method (FEM) is a popular tool in the community because of its accurate result, however, the FEM is computationally expensive to handle a large number of DOFs. We present novel techniques to combine linear and nonlinear elasticity with model reduction to provide fast and realistic animation. On the other hand, one of the most important computation tasks of solid simulation is to evaluate the gradient vector and Hessian matrix of elastic energy function. We present a numerical routine to simplify the implementation of solid simulation with the complex-step finite difference (CSFD) that avoids subtractive cancellation. The complexity of nonlinearity is also an obstacle, and we provide a framework called NNWarp to combine the linear elasticity and neural network-based warping method to avoid expensive nonlinear optimization. We also propose an acoustic-VR system as the application. The system can convert acoustic signals of human language to realistic 3D tongue animation in real-time. The Deep Neural Networks (DNN) helps to convert the input speaking voice to positions of pre-defined EMA sensors. Then, a novel reduced physics-based solid simulator, introduced in previous, is used to synthesis the tongue animation.


deformable model, FEM, model reduction, domain decomposition, physics-based simulation

Document Type




Degree Name

Computer Engineering

Level of Degree


Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Yin Yang

Second Committee Member

Marios Pattichis

Third Committee Member

Rafael Fierro

Fourth Committee Member

Meeko Oishi

Fifth Committee Member

Shuang Luan