설계 종속 압력 하중을 받는 위상 최적화를 위한 Python 코드: PyTOPress
PyTOPress는 설계 종속 압력 하중을 받는 구조물의 위상 최적화를 위해 Python에서 개발된 교육용 코드로, MATLAB에 대한 오픈 소스 대안을 제공합니다.
設計依存圧力荷重下におけるトポロジー最適化のためのPythonコード：PyTOPress
PyTOPressは、設計依存圧力荷重を受ける構造物のトポロジー最適化のためのコンパクトなPythonコードであり、教育目的で開発され、オープンソースで利用可能です。
PyTOPress: A Python Code for Topology Optimization of Structures Under Design-Dependent Pressure Loads
PyTOPress, a new open-source Python code, offers an accessible and efficient approach to topology optimization for structures subject to design-dependent pressure loads, leveraging the capabilities of Python libraries like NumPy and SciPy.
GridapTopOpt.jl: 확장 가능한 레벨셋 기반 위상 최적화를 위한 Julia 툴박스
GridapTopOpt.jl은 Julia로 작성된 레벨셋 기반 위상 최적화를 위한 확장 가능한 프레임워크로, 개인용 컴퓨터 또는 고성능 컴퓨팅 클러스터에서 쉽게 배포하여 사용할 수 있으며, 사용자 친화적인 인터페이스와 자동 미분 기능을 제공하여 다양한 위상 최적화 문제를 효율적으로 해결할 수 있습니다.
GridapTopOpt.jl：レベルセットベースのトポロジー最適化のためのスケーラブルなJuliaツールボックス
GridapTopOpt.jlは、レベルセットベースのトポロジー最適化問題を解決するためのスケーラブルで拡張性の高いJuliaツールボックスであり、直感的なインターフェース、自動微分、並列計算機能を提供し、大規模で複雑な問題への対応を容易にする。
GridapTopOpt.jl: A User-Friendly Julia Toolbox for Scalable Level Set-Based Topology Optimization Using Distributed Computing and Automatic Differentiation
GridapTopOpt.jl is a new Julia package that simplifies level set-based topology optimization, offering scalability for large problems and user-friendliness for both beginners and experts.
Parallel Heat Transfer Topology Optimization with Multiscale Multigrid Preconditioner for High-Resolution and High-Contrast Simulations
This paper introduces a parallel solver that efficiently optimizes material distribution for heat transfer in high-resolution, high-contrast scenarios by combining an interpolation technique with a robust multiscale multigrid preconditioner.
TOPress3D: An Educational MATLAB Code for 3D Topology Optimization with Design-Dependent Pressure Loads
This paper introduces TOPress3D, an open-source MATLAB code for 3D topology optimization of structures subject to design-dependent pressure loads, aiming to provide an accessible and pedagogical tool for students, researchers, and newcomers in the field.
Gradient-free Neural Topology Optimization: Enhancing Efficiency with Latent Reparameterization
Neural reparameterization in latent space significantly improves gradient-free topology optimization efficiency.
Gradient-free Neural Topology Optimization: Enhancing Efficiency with Latent Space Reparameterization
Pre-trained neural reparameterization in latent space significantly improves gradient-free topology optimization efficiency.