RTSG

Rubinstein, Dmitri; Georgiev, Iliyan; Schug, Benjamin; Slusallek, Philipp

doi:10.1145/1559764.1559771

Cited by 14 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parallel computation is crucial for phase-field codes to reduce the run times of large simulations. Using features of the deal.II 48 , p4est 62 , and Threading Building Blocks 63 libraries, PRISMS-PF employs three levels of parallelism: distributed memory parallelization, task-based threading, and vectorization. On the distributed memory level, the domain is decomposed such that each core stores only the refined mesh and corresponding degrees of freedom for its subdomain.…”

Section: Multilevel Parallelismmentioning

confidence: 99%

PRISMS-PF: A general framework for phase-field modeling with a matrix-free finite element method

et al. 2020

View full text Add to dashboard Cite

A new phase-field modeling framework with an emphasis on performance, flexibility, and ease of use is presented. Foremost among the strategies employed to fulfill these objectives are the use of a matrix-free finite element method and a modular, application-centric code structure. This approach is implemented in the new open-source PRISMS-PF framework. Its performance is enabled by the combination of a matrix-free variant of the finite element method with adaptive mesh refinement, explicit time integration, and multilevel parallelism. Benchmark testing with a particle growth problem shows PRISMS-PF with adaptive mesh refinement and higher-order elements to be up to 12 times faster than a finite difference code employing a second-order-accurate spatial discretization and first-order-accurate explicit time integration. Furthermore, for a two-dimensional solidification benchmark problem, the performance of PRISMS-PF meets or exceeds that of phase-field frameworks that focus on implicit/semi-implicit time stepping, even though the benchmark problem's small computational size reduces the scalability advantage of explicit timeintegration schemes. PRISMS-PF supports an arbitrary number of coupled governing equations. The code structure simplifies the modification of these governing equations by separating their definition from the implementation of the numerical methods used to solve them. As part of its modular design, the framework includes functionality for nucleation and polycrystalline systems available in any application to further broaden the phenomena that can be used to study. The versatility of this approach is demonstrated with examples from several common types of phase-field simulations, including coarsening subsequent to spinodal decomposition, solidification, precipitation, grain growth, and corrosion.

show abstract

Section: Multilevel Parallelismmentioning

confidence: 99%

PRISMS-PF: A general framework for phase-field modeling with a matrix-free finite element method

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The exact path corrects SBR ray paths so that they end at the exact receiver location. This correction reduces errors in calculated power and phase associated with SBR but at the cost of longer run times required by methods based on image theory [37]- [39].…”

Section: A Remcom Wireless Insite For Path Loss Generationmentioning

confidence: 99%

5G mmWave Network Planning Using Machine Learning for Path Loss Estimation

Santana,

Alonso,

Nieto

et al. 2024

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

New frequency bands, such as the mmWave at 28 GHz for fifth Generation networks in Frequency Range 2, are being introduced to accomplish the required throughput for new services such as remote surveillance, object tracking, and factory automation. These high-frequency bands are sensible to reflections and diffraction. Therefore, accurate path loss calculation relies on advanced models and ray tracing. However, this is time-consuming, and evaluating a large set of candidate solutions is no longer possible when planning the optimal number and location of base stations in the network planning process. This paper investigates the use of machine learning to approximate a complex mmWave ray-tracingbased path loss model in indoor scenarios. The much lower calculation time allows us to approximate the ray-tracer path loss estimation well and to apply a genetic algorithm for realizing network planning. The machine learning model is trained and validated for two buildings and tested with another, with an average of the Mean Absolute Error of 2.8 dB over all cases. It is shown that the combination of Machine Learning and Genetic Algorithm is able to find a network deployment in the FR2 band accounting for the minimum number of access points and minimum electromagnetic exposure, while still providing a predefined coverage percentage inside the building.

show abstract