Merge branch 'main' of https://github.com/cnr-isti-vclab/cnr-isti-vcl…

…ab.github.io

content/project/sun/index.md

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+---
+# Documentation: https://wowchemy.com/docs/managing-content/
+
+title: "SUN-XR"
+summary: ""
+authors: []
+tags: []
+categories: []
+date: 2023-01-20T10:39:08+01:00
+
+# Optional external URL for project (replaces project detail page).
+external_link: "https://evocation.eu/"
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder.
+# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
+image:
+  caption: ""
+  focal_point: ""
+  preview_only: false
+
+# Custom links (optional).
+#   Uncomment and edit lines below to show custom links.
+# links:
+# - name: Follow
+#   url: https://twitter.com
+#   icon_pack: fab
+#   icon: twitter
+
+url_code: ""
+url_pdf: ""
+url_slides: ""
+url_video: ""
+
+# Slides (optional).
+#   Associate this project with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides = "example-slides"` references `content/slides/example-slides.md`.
+#   Otherwise, set `slides = ""`.
+slides: ""
+---

content/publication/2022/LGMCF22/index.md

@@ -18,4 +18,8 @@ image:
     share: false
 url_pdf: http://vcg.isti.cnr.it/Publications/2022/LGMCF22/Laccone_Vorogrid_CACAIE_postprint_compressed.pdf
 abstract: 'In the context of tall building design, the tube concept represents one of the most performing systems. The diagrid is the widespread type of tube system and consists of a diagonal grid of beams that wraps the building, forming a diamond pattern. It performs as lateral bracing and is additionally able to sustain vertical loading through axial forces. Despite its efficiency, a growing interest is recently observed in alternative geometries to replace the diagrid pattern and improve the architectural impact conferred by the building skin aesthetics on the urban environment. The paper pursues the use of a Voronoi mesh, in which the geometry of the cells is steered to known schemes for the structural design of a cantilever tube structure. The objective is to mimic a macroscopic structural behavior through a topology and size modification of the Voronoi mesh that increases the density for creating resisting paths with higher stiffness. The paper proposes a novel method Vorogrid for designing a new class of tall buildings equipped with an organic-looking and mechanically-sound tube structure, which makes them a valuable alternative to competitors (diagrid, hexagrid, random Voronoi). Diagrids and hexagrids still remain more efficient in terms of forces and displacements but are characterized by a more usual appearance, instead Vorogrid offers more design control and better performances on average with respect to random Voronoi structures. This method is streamed into a pipeline that includes grid initialization strategies, geometric and structural optimization to mitigate the effects of the grid randomness, and structural sizing.'
+
+tags:
+ - Architectural Geometry
+ - Structural Optimization
 ---

content/publication/2024/FLCMG24-GeometricLearningxShells/cite.bib

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+@article{favilli2024geometric,
+  title = {Geometric deep learning for statics-aware grid shells},
+  journal = {Computers \& Structures},
+  volume = {292},
+  pages = {107238},
+  year = {2024},
+  issn = {0045-7949},
+  doi = {10.1016/j.compstruc.2023.107238},
+  url = {https://www.sciencedirect.com/science/article/pii/S0045794923002687},
+  author = {Andrea Favilli and Francesco Laccone and Paolo Cignoni and Luigi Malomo and Daniela Giorgi},
+  keywords = {Grid shell, Freeform surface, Form finding, Shape optimization, Structural design, Automatic differentiation},
+  abstract = {This paper introduces a novel method for shape optimization and form-finding of free-form, triangular grid shells, based on geometric deep learning. We define an architecture which consumes a 3D mesh representing the initial design of a free-form grid shell, and outputs vertex displacements to get an optimized grid shell that minimizes structural compliance, while preserving design intent. The main ingredients of the architecture are layers that produce deep vertex embeddings from geometric input features, and a differentiable loss implementing structural analysis. We evaluate the method performance on a benchmark of eighteen free-form grid shell structures characterized by various size, geometry, and tessellation. Our results demonstrate that our approach can solve the shape optimization and form finding problem for a diverse range of structures, more effectively and efficiently than existing common tools.}
+}

content/publication/2024/FLCMG24-GeometricLearningxShells/index.md

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+---
+# delete the following three lines if you want that your page appears:
+#_build:
+#  render: always
+#  list: never
+
+title: 'Geometric deep learning for statics-aware grid shells'
+authors:
+  - Andrea Favilli
+  - Francesco Laccone 
+  - Paolo Cignoni 
+  - Luigi Malomo 
+  - Daniela Giorgi
+
+
+date: '2024-02-01T00:00:00Z'
+doi: '10.1016/j.compstruc.2023.107238'
+
+# Schedule page publish date (NOT publication's date).
+publishDate: '2017-01-01T00:00:00Z'
+
+# Publication type.
+# Legend: 0 = Uncategorized; 1 = Conference paper; 2 = Journal article;
+# 3 = Preprint / Working Paper; 4 = Report; 5 = Book; 6 = Book section;
+# 7 = Thesis; 8 = Patent
+publication_types: ['2']
+
+# Publication name and optional abbreviated publication name.
+publication: 'Computers & Structures'
+publication_short: ''
+
+abstract: This paper introduces a novel method for shape optimization and form-finding of free-form, triangular grid shells, based on geometric deep learning. We define an architecture which consumes a 3D mesh representing the initial design of a free-form grid shell, and outputs vertex displacements to get an optimized grid shell that minimizes structural compliance, while preserving design intent. The main ingredients of the architecture are layers that produce deep vertex embeddings from geometric input features, and a differentiable loss implementing structural analysis. We evaluate the method performance on a benchmark of eighteen free-form grid shell structures characterized by various size, geometry, and tessellation. Our results demonstrate that our approach can solve the shape optimization and form finding problem for a diverse range of structures, more effectively and efficiently than existing common tools.
+
+# Summary. An optional shortened abstract.
+summary: 
+
+tags:
+  - Architectural Geometry
+  - Grid Shells
+  - Shape Optimization
+  - Form Finding
+  - evocation
+  - Sun
+featured: true
+
+# links:
+# - name: ""
+#   url: ""
+url_pdf: https://vcgdata.isti.cnr.it/Publications/2024/FLCMG24-GeometricLearningxShells/FLCMG24-GeometricLearningxShells.pdf
+url_code: 'https://github.com/cnr-isti-vclab/GeomDL4GridShell#geometric-deep-learning-for-statics-aware-grid-shells'
+url_dataset: ''
+url_poster: ''
+url_project: ''
+url_slides: ''
+url_source: ''
+url_video: ''
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder.
+image:
+  caption: ''
+  focal_point: ''
+  preview_only: false
+
+# Associated Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `internal-project` references `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+projects: []
+
+# Slides (optional).
+#   Associate this publication with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides: "example"` references `content/slides/example/index.md`.
+#   Otherwise, set `slides: ""`.
+slides:
+---

content/publication/conference-paper/index.md

@@ -45,6 +45,8 @@ url_slides: ''
 url_source: '#'
 url_video: '#'
 
+youtubeId: spjTRArOeNY
+
 # Featured image
 # To use, add an image named `featured.jpg/png` to your page's folder.
 image: