Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh ...Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh adaptation provides an optimal resource allocation to obtain high-resolution flow-fields on low-resolution meshes.However,most existing methods require manual experience and the flow posteriori information poses great challenges to practical applications.In addition,generating adaptive meshes directly from design parameters is difficult due to highly nonlinear relationships.The diffusion model is currently the most popular model in generative tasks that integrates the diffusion principle into deep learning to capture the complex nonlinear correlations.A dual diffusion framework,Para2Mesh,is proposed to predict the adaptive meshes from design parameters by exploiting the robust data distribution learning ability of the diffusion model.Through iterative denoising,the proposed dual networks accurately reconstruct the flow-field to provide flow features as supervised information,and then achieve rapid and reliable mesh movement.Experiments in CFD scenarios demonstrate that Para2Mesh predicts similar meshes directly from design parameters with much higher efficiency than traditional method.It could become a real-time adaptation tool to assist engineering design and optimization,providing a promising solution for high-resolution flow-field analysis.展开更多
Form-finding is a process in architectural design.Architects create and manipulate the morphology of a building by finding the form using digital tools and algorithms,such as machine learning.Recent research indicates...Form-finding is a process in architectural design.Architects create and manipulate the morphology of a building by finding the form using digital tools and algorithms,such as machine learning.Recent research indicates that existing machine learning methods for architectural form-finding are not efficient for training and cannot generate multiple 3D forms under the constraints of users.Therefore,in this research,we develop a method to train and apply low-rank adaptation(LoRA)models in Stable Diffusion(SD)to generate 3D architectural forms based on morphological heat maps.Furthermore,the generated 3D forms can be directly used to precisely control the generation of realistic architectural renderings using pre-trained LoRA and SD models.In conclusion,our method can help architects generate 3D architectural models with consistent renderings.It can serve as a useful tool to improve efficiency and creativity in the architectural design practice of form-finding.展开更多
We consider the finite element based computation of topological quantities of implicitly represented surfaces within a diffuse interface framework.Utilizing an adaptive finite element implementation with effective gra...We consider the finite element based computation of topological quantities of implicitly represented surfaces within a diffuse interface framework.Utilizing an adaptive finite element implementation with effective gradient recovery techniques,we discuss how the Euler number can be accurately computed directly from the numerically solved phase field functions or order parameters.Numerical examples and applications to the topological analysis of point clouds are also presented.展开更多
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002 and 2019ZA052011)。
文摘Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh adaptation provides an optimal resource allocation to obtain high-resolution flow-fields on low-resolution meshes.However,most existing methods require manual experience and the flow posteriori information poses great challenges to practical applications.In addition,generating adaptive meshes directly from design parameters is difficult due to highly nonlinear relationships.The diffusion model is currently the most popular model in generative tasks that integrates the diffusion principle into deep learning to capture the complex nonlinear correlations.A dual diffusion framework,Para2Mesh,is proposed to predict the adaptive meshes from design parameters by exploiting the robust data distribution learning ability of the diffusion model.Through iterative denoising,the proposed dual networks accurately reconstruct the flow-field to provide flow features as supervised information,and then achieve rapid and reliable mesh movement.Experiments in CFD scenarios demonstrate that Para2Mesh predicts similar meshes directly from design parameters with much higher efficiency than traditional method.It could become a real-time adaptation tool to assist engineering design and optimization,providing a promising solution for high-resolution flow-field analysis.
基金funded by the New Faculty Start-up Grant(Project No.9610653)from the City University of Hong Kong.
文摘Form-finding is a process in architectural design.Architects create and manipulate the morphology of a building by finding the form using digital tools and algorithms,such as machine learning.Recent research indicates that existing machine learning methods for architectural form-finding are not efficient for training and cannot generate multiple 3D forms under the constraints of users.Therefore,in this research,we develop a method to train and apply low-rank adaptation(LoRA)models in Stable Diffusion(SD)to generate 3D architectural forms based on morphological heat maps.Furthermore,the generated 3D forms can be directly used to precisely control the generation of realistic architectural renderings using pre-trained LoRA and SD models.In conclusion,our method can help architects generate 3D architectural models with consistent renderings.It can serve as a useful tool to improve efficiency and creativity in the architectural design practice of form-finding.
基金supported in part by US NSF-DMS 1016073,NSFC 11271350 and 91130019Special Research Funds for State Key Laboratories Y22612A33S+1 种基金China 863 project 2010AA012301 and 2012AA01A304China 973 project 2011CB309702.
文摘We consider the finite element based computation of topological quantities of implicitly represented surfaces within a diffuse interface framework.Utilizing an adaptive finite element implementation with effective gradient recovery techniques,we discuss how the Euler number can be accurately computed directly from the numerically solved phase field functions or order parameters.Numerical examples and applications to the topological analysis of point clouds are also presented.
