The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

The goal of this experiment was to examine the energy absorption properties of origami-inspired honeycomb and standard honeycomb structures. These structures were 3D printed with two different materials: thermoplastic polyurethane (TPU) and acrylonitrile butadiene styrene (ABS). Quasi-static compression testing was performed on these structures for both types and materials at various wall thicknesses. The energy absorption and other material properties were analyzed for each structure. Overall, the results indicate that origami-inspired structures perform best at energy absorption at a higher wall thickness with a rigid material. The results also indicated that standard honeycomb structures perform better with lower wall thickness, and also perform better with a rigid, rather than a flexible material. Additionally, it was observed that a flexible material, like TPU, better demonstrates the folding and recovery properties of origami-inspired structures. The results of this experiment have applications wherever honeycomb structures are used, mostly on aircraft and spacecraft. In vehicles with structures of a sufficiently high wall thickness with a rigid material, origami-inspired honeycomb structures could be used instead of current honeycomb structures in order to better protect the passengers or payload through improved energy absorption.