Description
- Abstract:
- This dissertation is composed around the subject of multiscale modeling of soft matter and biophysical systems with applications using large-scale computations. Specifically, it is expanded on three fronts: 1) Development of high-performance simulators and computational frameworks. On this front, I will discuss the design of three sets of software. The first one is an accelerated parallel particle simulator, which features many algorithmic innovations for harnessing the massively parallel threading architecture of general purpose graphics processing units. The second one is an ultrafast coarse-grained molecular dynamics simulator, which enables the simulation of an entire human red blood cell at protein resolution using a single computer workstation. This is realized by a novel algorithm that allows neighbor search in a sparse 3D space in linear time.The third one is a generic framework that utilizes the concept of meshless interpolation to faciliate the implementation of parallel concurrently coupled multiscale simulations. 2) Construction of mesoscopic models for amphiphilic and thermo-responsive polymers and their applications to large-scale mesoscopic simulations. This is manifested in a detailed study of the non-equilibrium dynamics of thermo-responsive polymers. One of the most interesting findings is that a thermo-responsive polymer membrane may invert its layered structure without actually rotating any of its composing molecules. 3) Data-driven algorithms for learning complex interatomic force fields. Here I have focus on a specific aspect of this field, i.e. the design of feature vectors that can efficiently and accurately quantify the similarity between atomistic configurations. To achieve this, a kernel minisum approach is proposed as a robust and efficient replacement of the principal component analysis algorithm. A set of quadrature rules and parameters are also proposed for constructing a smoothed density field that allows either inner product- or norm-based comparison of structural similarity.
- Notes:
- Thesis (Ph. D.)--Brown University, 2017
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Citation
Tang, Yu-Hang,
"Multiscale and Mesoscopic Modeling of Soft Matter and Biophysical Systems Using High Performance Computing and Machine Learning"
(2017).
Applied Mathematics Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.26300/a0qn-1a06
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Applied Mathematics Theses and Dissertations
Theses and Dissertations for the Applied Mathematics department....