Replication Data for: A Deep-Learning Based Incidence Operator for Adjustable and Solver-Agnostic Modelling of Adaptive Façades

DOI

This dataset contains the files:
- code: All Python files to replicate result data, figure and table generation.
- data/raw: The training data including all investigated feature sets as well as the target. The training data for the incidence operator (y) were generated using the Grasshopper file and the plugins presented in the study (https://doi.org/10.18419/opus-17170). For the feature matrix X1, a uniform grid of 11 tint states between [0% and 100%] was generated. The 10,000 zenith and azimuth angles per discrete tint state are determined using the Fibonacci distribution. The feature sets X2,X3,X4 are derived from X1 as described in the paper. Additionally, this folder contains the Grasshopper file with all information about the investigated parametric design. - data/results: The Optuna study results for all feature sets. - figures: The result figures within the paper.

Identifier
DOI https://doi.org/10.18419/DARUS-5569
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-5569
Provenance
Creator Seddik, Moustafa ORCID logo; Weber, Simon Oskar ORCID logo; Leistner, Philip ORCID logo
Publisher DaRUS
Contributor Weber, Simon Oskar
Publication Year 2025
Funding Reference DFG 279064222
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Weber, Simon Oskar (University of Stuttgart); Weber, Simon Oskar (Private)
Representation
Resource Type Dataset
Format application/octet-stream; image/png; text/x-python; text/plain
Size 61714; 112703; 32253; 150763; 27311; 21979; 2202347; 2377277; 3049589; 3175869; 311529; 327570; 327073; 331424; 312042; 331228; 314482; 331868; 871241; 1329; 0; 3379; 8714; 150; 258048; 16555; 11647; 2640128; 3520128; 151360128; 152240128; 880128
Version 2.0
Discipline Chemistry; Construction Engineering and Architecture; Design; Engineering; Engineering Sciences; Fine Arts, Music, Theatre and Media Studies; Humanities; Natural Sciences; Physics