Mathematical definitions provide a precise, unambiguous way to formulate concepts. They also provide a common language between disciplines. Thus, they are the basis for a well-founded scientific discussion. In addition, mathematical definitions allow for deeper insights into the defined subject based on mathematical theorems that are incontrovertible under the given definition. Besides their value in mathematics, mathematical definitions are indispensable in other sciences like physics, chemistry, and computer science. In computer science, they help to derive the expected behavior of a computer program and provide guidance for the design and testing of software. Therefore, mathematical definitions can be used to design and implement advanced algorithms.
One class of widely used algorithms in computer science is the class of particle-based algorithms, also known as particle methods. Particle methods can solve complex problems in various fields, such as fluid dynamics, plasma physics, or granular flows, using diverse simulation methods, including Discrete Element Methods (DEM), Molecular Dynamics (MD), Reproducing Kernel Particle Methods (RKPM), Particle Strength Exchange (PSE), and Smoothed Particle Hydrodynamics (SPH). Despite the increasing use of particle methods driven by improved computing performance, the relation between these algorithms remains formally unclear. In particular, particle methods lack a unifying mathematical definition and precisely defined terminology. This prevents the determination of whether an algorithm belongs to the class and what distinguishes the class.
Here we present a rigorous mathematical definition for determining particle methods and demonstrate its importance by applying it to several canonical algorithms and those not previously recognized as particle methods. Furthermore, we base proofs of theorems about parallelizability and computational power on it and use it to develop scientific computing software.
Our definition unified, for the first time, the so far loosely connected notion of particle methods. Thus, it marks the necessary starting point for a broad range of joint formal investigations and applications across fields.:1 Introduction
1.1 The Role of Mathematical Definitions
1.2 Particle Methods
1.3 Scope and Contributions of this Thesis
2 Terminology and Notation
3 A Formal Definition of Particle Methods
3.1 Introduction
3.2 Definition of Particle Methods
3.2.1 Particle Method Algorithm
3.2.2 Particle Method Instance
3.2.3 Particle State Transition Function
3.3 Explanation of the Definition of Particle Methods
3.3.1 Illustrative Example
3.3.2 Explanation of the Particle Method Algorithm
3.3.3 Explanation of the Particle Method Instance
3.3.4 Explanation of the State Transition Function
3.4 Conclusion
4 Algorithms as Particle Methods
4.1 Introduction
4.2 Perfectly Elastic Collision in Arbitrary Dimensions
4.3 Particle Strength Exchange
4.4 Smoothed Particle Hydrodynamics
4.5 Lennard-Jones Molecular Dynamics
4.6 Triangulation refinement
4.7 Conway's Game of Life
4.8 Gaussian Elimination
4.9 Conclusion
5 Parallelizability of Particle Methods
5.1 Introduction
5.2 Particle Methods on Shared Memory Systems
5.2.1 Parallelization Scheme
5.2.2 Lemmata
5.2.3 Parallelizability
5.2.4 Time Complexity
5.2.5 Application
5.3 Particle Methods on Distributed Memory Systems
5.3.1 Parallelization Scheme
5.3.2 Lemmata
5.3.3 Parallelizability
5.3.4 Bounds on Time Complexity and Parallel Scalability
5.4 Conclusion
6 Turing Powerfulness and Halting Decidability
6.1 Introduction
6.2 Turing Machine
6.3 Turing Powerfulness of Particle Methods Under a First Set of Constraints
6.4 Turing Powerfulness of Particle Methods Under a Second Set of Constraints
6.5 Halting Decidability of Particle Methods
6.6 Conclusion
7 Particle Methods as a Basis for Scientific Software Engineering
7.1 Introduction
7.2 Design of the Prototype
7.3 Applications, Comparisons, Convergence Study, and Run-time Evaluations
7.4 Conclusion
8 Results, Discussion, Outlook, and Conclusion
8.1 Problem
8.2 Results
8.3 Discussion
8.4 Outlook
8.5 Conclusion
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:85776 |
| Date | 05 June 2023 |
| Creators | Pahlke, Johannes |
| Contributors | Sbalzarini, Ivo F., Hebisch, Udo, Lehner, Wolfgang, Technische Universität Dresden, Max-Planck-Institut für molekulare Zellbiologie und Genetik |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | info:eu-repo/grantAgreement/Deutsche Forschungsgemeinschaft/Graduiertenkolleg/GRK 1907//Rollenbasierte Software-Infrastrukturen für durchgängig-kontextsensitive Systeme /RoSI |
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