AP47: Multi-objective optimisation and machine learning for climate-friendly building design
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61 to 70 of 137 Results
JSON - 3.8 MB - MD5: d6ff065f4cc8615cd1df121f258def8f
Raw simulation input data (geometry, construction, climate) for design space scenario 08.
JSON - 3.8 MB - MD5: e0f673646d0be17f6a707c7e6d808b25
Raw simulation input data (geometry, construction, climate) for design space scenario 09.
JSON - 4.3 MB - MD5: 22b879bed0a2d1e557bb6b447065f7d7
Raw simulation input data (geometry, construction, climate) for design space scenario 10.
Python Source Code - 13.6 KB - MD5: 054939eba0653d063d33486a9f57f85e
Maintains a registry/catalog of available feature representations and their configurations.
Python Source Code - 3.4 KB - MD5: 2579648b102bbde1f1aeb27eb974e0a8
Orchestrates the computation of feature vectors for a given dataset and representation type.
Python Source Code - 693 B - MD5: 01f7f26d72818a87c47522af2b805f50
Central configuration file defining datasets, representations, objectives, and file paths used by the pipeline.
Python Source Code - 1.5 KB - MD5: 153d581913901949ec61912b5f4366ed
Defines the schema for construction assembly (material/layer) data.
Unknown - 3.6 KB - MD5: 89a1c9cd0c0965e267ed56db9a186a88
Conda environment specification listing all Python dependencies required to run the pipeline.
Python Source Code - 745 B - MD5: 37007ef3fabad906a4d421787af48b60
Loads and parses raw JSON input files into structured data objects.
Python Source Code - 1.1 KB - MD5: 29e4eda24f21801e855c5c10d697a3c5
Entry point script that runs the full preprocessing pipeline end-to-end.
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