Sökning: "computational topology"

Visar resultat 1 - 5 av 28 uppsatser innehållade orden computational topology.

1. 1. Analysis of Mutable Game Environments Built on a Tetrahedral Mesh : Tetras, a Potential Alternative to Voxels

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Noah Tell; [2023]
   Nyckelord :Voxels; Tetrahedron; Tetrahedral Mesh; Tetrahedralization; Tessellation; Mutable Environment; Editable Terrain; Deformable Environment; Boolean Operations; Real-Time; Computer Games; Computational Geometry; Voxlar; Tetrahedror; Tetrahedralisering; Tessellering; Formbar Terräng; Booliska Operationer; Realtid; Datorspel; Beräkningsgeometri;

   Sammanfattning : Historically 3D game environments have almost always been immutable. Mutable environments are a technical challenge that will affect performance. For games of the future to continue approaching realism, mutable environments are an essential step. LÄS MER

3. 2. Design of cooling-air permeable coil support

   Master-uppsats, KTH/Hållfasthetslära

   Författare :Rozbeh Ghassemi; [2023]
   Nyckelord :Topology optimization; FEM-simulations; CFD-simulations; Material selection; Topologioptimering; FEM-simuleringar; CFD-simuleringar; Materialval;

   Sammanfattning : Coil supports are integral load-bearing components employed in generators andmotors. They serve the purpose of preventing excessive deformation and maintaininga stable position of the coils responsible for generating power and magnetic fieldswhen rotating. LÄS MER

4. 3. Topology optimization: perimeter restriction using total variation

   Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Hållfasthetslära; Lunds universitet/Institutionen för byggvetenskaper

   Författare :Jonas Fredriksson; [2022]
   Nyckelord :Topology optimization; structural optimization; solid mechanics; total variation; Technology and Engineering;

   Sammanfattning : Topology optimization is a method used to find optimal material distributions, within a specified domain, with respect to some performance measure. To avoid various artifacts to appear in the suggested design, the solution space is typically restricted, where some restriction methods allow different length scales to be controlled in the design. LÄS MER

5. 4. Exploring persistent homology as a method for capturing functional connectivity differences in Parkinson’s Disease.

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Naomi Hulst; [2022]
   Nyckelord :persistent homology; topological data analysis; computational topology; parkinson s disease; parkinson; functional connectivity; stable rank; ihållande homologi; topologisk data analys; beräknings topologi; Parkinsons sjukdom; parkinson; funktionell konnektivitet; stable ranks;

   Sammanfattning : Parkinson’s Disease (PD) is the fastest growing neurodegenerative disease, currently affecting two to three percent of the population over 65. Studying functional connectivity (FC) in PD patients may provide new insights into how the disease alters brain organization in different subjects. LÄS MER

7. 5. Towards topology-aware Variational Auto-Encoders : from InvMap-VAE to Witness Simplicial VAE

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Aniss Aiman Medbouhi; [2022]
   Nyckelord :Variational Auto-Encoder; Nonlinear dimensionality reduction; Generative model; Inverse projection; Computational topology; Algorithmic topology; Topological Data Analysis; Data visualisation; Unsupervised representation learning; Topological machine learning; Betti number; Simplicial complex; Witness complex; Simplicial map; Simplicial regularization.; Variations autokodare; Ickelinjär dimensionalitetsreducering; Generativ modell; Invers projektion; Beräkningstopologi; Algoritmisk topologi; Topologisk Data Analys; Datavisualisering; Oövervakat representationsinlärning; Topologisk maskininlärning; Betti-nummer; Simplicielt komplex; Vittneskomplex; Simpliciel avbildning; Simpliciel regularisering.;

   Sammanfattning : Variational Auto-Encoders (VAEs) are one of the most famous deep generative models. After showing that standard VAEs may not preserve the topology, that is the shape of the data, between the input and the latent space, we tried to modify them so that the topology is preserved. LÄS MER