Publication Date
Spring 2024
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Engineering
Advisor
Stas Tiomkin; Feruza Amirkulova; Magdalini Eirinaki
Abstract
Partial Differential Equations (PDEs) are foundational descriptive tools in scientific and engineering disciplines, enabling the exploration of phenomena from material design to climate dynamics and the propagation of pandemics and wildfires. Establishing control of PDEs, especially when the underlying phenomena are only partially observable, holds great potential for fostering technological innovations. This ambition faces considerable obstacles, as a robust sample-based solution remains elusive. Addressing this challenge, this thesis introduces a framework leveraging physics-informed machine learning (ML) to enable control of PDEs. Central to this framework is an agent, equipped with sensors to perceive its environment, that generates a low-dimensional physics-informed representation of the environment. This representation enables the derivation of an optimal policy for sparse control by the agent’s actuators. The framework provides a novel objective function which guides the agent to learn from its environment. Two alternative implementations of the objective are shown in the particular setting of the acoustic wave equation. One implementation employs differentiable numerical integration, while the other relies on physics-informed neural networks (PINN). The superiority of these approaches are demonstrated in prediction and control of scattered acoustic wave energy compared to the state-of-the-art methods.
Recommended Citation
Shah, Tristan, "Controllable Physics-Informed Latent Space for Acoustic Wave Manipulation" (2024). Master's Theses. 5524.
DOI: https://doi.org/10.31979/etd.w6cc-46xt
https://scholarworks.sjsu.edu/etd_theses/5524
