Learning and Simulation Algorithms for Constraint Physical Systems

This thesis explores two computational approaches to learn and simulate complex physical systems exhibiting constraint characteristics. The target applications encompass both solids and fluids. On the solid side, we proposed a new family of data-driven simulators to predict the behaviors of an unknown physical system by learning its underpinning constraints. We devised a neural projection operator facilitated by an embedded recursive neural network to interactively enforce the learned underpinning constraints and to predict its various physical behaviors. Our method can automatically uncover a broad range of constraints from observation point data, such as length, angle, bending, collision, boundary effects, and their combinations, in the context of a diverse set of physical systems including rigid bodies, ropes, articulated bodies, and multi-object collisions. On the fluid side, we proposed a gauge numerical simulator to model fluid phenomena using Clebsch wave functions. Our method combines the expressive power of Clebsch wave functions to represent coherent vortical structures and the generality of gauge methods to accommodate a broad array of fluid phenomena. We devised a transformed wave function as the system’s gauge variable to improve a fluid simulator’s vorticity generation and preservation ability. We showcase our method by simulating various types of incompressible flow phenomena, including complex vortex filament dynamics, fluids with different obstacles, and surface-tension flow

Synthesis and stabilisation of novel UV absorbers

Plants can respond differently to different wavelengths in sunlight’s spectral range, and crop covers containing additives have a great effect on the growth of crops. This research focuses on synthesising new hydroxybenzophenones bearing long alkyl chains to confer polymer solubility, and to measure their UV absorption and photochemical stability. Compounds substituted with fluorine atoms or different amino groups in particular were under investigated, as these groups may impart stability towards oxidative degradation, or alter the absorption maximum. Related naphthalene analogues were substituted with different amine groups for comparing UV absorption and photostability. Modification of Uvinul A Plus was carried out to improve UV absorption maximum wavelength and light fastness. [Continues.

Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation

Contemporary domain adaptation offers a practical solution for achieving cross-domain transfer of semantic segmentation between labeled source data and unlabeled target data. These solutions have gained significant popularity; however, they require the model to be retrained when the test environment changes. This can result in unbearable costs in certain applications due to the time-consuming training process and concerns regarding data privacy. One-shot domain adaptation methods attempt to overcome these challenges by transferring the pre-trained source model to the target domain using only one target data. Despite this, the referring style transfer module still faces issues with computation cost and over-fitting problems. To address this problem, we propose a novel framework called Informative Data Mining (IDM) that enables efficient one-shot domain adaptation for semantic segmentation. Specifically, IDM provides an uncertainty-based selection criterion to identify the most informative samples, which facilitates quick adaptation and reduces redundant training. We then perform a model adaptation method using these selected samples, which includes patch-wise mixing and prototype-based information maximization to update the model. This approach effectively enhances adaptation and mitigates the overfitting problem. In general, we provide empirical evidence of the effectiveness and efficiency of IDM. Our approach outperforms existing methods and achieves a new state-of-the-art one-shot performance of 56.7\%/55.4\% on the GTA5/SYNTHIA to Cityscapes adaptation tasks, respectively. The code will be released at \url{https://github.com/yxiwang/IDM}.Comment: Accepted by ICCV 202

2021-2022 Master Class - Dr. Jon Robertson (Piano)

https://spiral.lynn.edu/conservatory_masterclasses/1232/thumbnail.jp