Institute of Computational Design and Construction

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

1 to 10 of 320 Results

ABxM.Core-1.7.0.zip Jun 5, 2025 - ABxM.Core: The Core Libraries of the ABxM Framework ZIP Archive - 7.4 MB - MD5: 69640fa696c81c93398350afb98da032
ABxM.Core.Help.chm Jun 5, 2025 - ABxM.Core: The Core Libraries of the ABxM Framework application/vnd.ms-htmlhelp - 519.1 KB - MD5: 10923118dea9f1d0b341d9249add8ab9
GP_00.json Mar 17, 2025 - Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates. JSON - 553.7 KB - MD5: 21610e5f675a9c616922962e836f1742
GP_01.json Mar 17, 2025 - Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates. JSON - 560.3 KB - MD5: 6a5507abfa6ca21b864210f8a9a720eb
GP_02.json Mar 17, 2025 - Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates. JSON - 546.2 KB - MD5: 1790b4a412ea8c0235809e20538e539f
GP_03.json Mar 17, 2025 - Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates. JSON - 546.2 KB - MD5: 7a0b79d1f887ac6dd9ce1c7d75fa8b28
GP_04.json Mar 17, 2025 - Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates. JSON - 555.3 KB - MD5: cce918a488a3074f5ab1ba3d0fe7b344
LHS_00.json Mar 17, 2025 - Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates. JSON - 1.3 MB - MD5: a6cbae1b4477d3e08c2775ccf15d5c68
LHS_01.json Mar 17, 2025 - Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates. JSON - 7.0 MB - MD5: 0287865a26350178b258a19a9103dc6a
MOEAD_00.json Mar 17, 2025 - Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates. JSON - 556.5 KB - MD5: 00ced999c97245139b970e7b95765ce5

ABxM.Core-1.7.0.zip

Jun 5, 2025 - ABxM.Core: The Core Libraries of the ABxM Framework

ZIP Archive - 7.4 MB -

ABxM.Core.Help.chm

Jun 5, 2025 - ABxM.Core: The Core Libraries of the ABxM Framework

application/vnd.ms-htmlhelp - 519.1 KB -

GP_00.json

Mar 17, 2025 - Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates.

JSON - 553.7 KB -

GP_01.json

Mar 17, 2025 - Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates.

JSON - 560.3 KB -