The vision of the Cluster of Excellence Integrative Computational Design and Construction for Transformative Architecture (EXC IntCDC) is to harness the full potential of digital technologies in order to rethink design, fabrication and construction based on integration and interdisciplinarity, with the goal of enabling game-changing innovation in the building sector as it can only occur through highly integrative fundamental research in an interdisciplinary, large-scale research undertaking.

The Cluster aims to lay the methodological foundations for a profound rethinking of the design and building process and related building systems by adopting an integrative computational approach based on interdisciplinary research encompassing architecture, structural engineering, building physics, engineering geodesy, manufacturing and system engineering, computer science and robotics, social sciences and humanities. We aim to bundle the internationally recognised competencies in these fields of the University of Stuttgart and the Max Planck Institute for Intelligent Systems to accomplish our research mission.

The Cluster’s Industry Consortium will ensure direct knowledge exchange, transfer and rapid impact. Taking into account the significant difference between the building industry and other industries, we will tackle the related key challenges of achieving a higher level of integration, performance and adaptability, and we will address the most important building typologies of multi-storey buildings, long-span buildings, and the densification of urban areas.

The Cluster’s broad methodological insights and interdisciplinary findings are expected to result in comprehensive approaches to harnessing digital technologies, which will help to address the ecological, economic and social challenges that current incremental approaches cannot solve.

We envision IntCDC to significantly shape the future of architecture and the building industry through a higher-level integration of computational design and engineering methods, effective cyber-physical (tightly interlinked computational and material) robotic construction processes and new forms of human-machine collaboration, efficient and sustainable next-generation building systems, and socio-cultural and ethical reflection. Thus, the Cluster will have significant impact on creating the conditions required for a liveable and sustainable future built environment, high-quality yet affordable architecture and a novel digital building culture.
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.

Advanced Search

111 to 120 of 2,999 Results
Compressed Archive - 272.5 KB - MD5: 52ccea3632338c50f38dd7cfec4ded47
Code
Source code of APTree version 2.0 (with replanning): DSL, execution engine, planning service, editor, analysis scripts, Lean proofs, Docker setup. Licensed under MIT. Extract next to the v2.0 data archive and see code/README.md.
Compressed Archive - 182.8 KB - MD5: bd923c399ff0ee57b6ac623f734c8fcb
Data
Data of APTree version 2.0 (with replanning): DSL models, runtime models, PDDL planner inputs, execution logs of the reported run. Licensed under CC BY 4.0. See data/README.md.
Oct 15, 2026 - EXC IntCDC Associated Project 47 'Optimisation and Machine Learning for Climate-Friendly Design'
Shakurianova, Alfiia; Skoury, Lior; Zorn, Max Benjamin; Wortmann, Thomas, 2026, "Hyperparameter Optimization: a Large-scale Benchmark Using Random Search, Sensitivity Analysis, and Surrogate Multi-objective Optimization on Black-box Problems", https://doi.org/10.18419/DARUS-6406, DaRUS, V1
Overview This project benchmarks ten multi-objective optimization algorithms, including NSGA-II, NSGA-III, SPEA2, MOEA/D, HYPE, NSPSO, MACO, RBFMOpt, TPE, and NOMAD, across a large and diverse set of black-box (bbcom) problems. The study evaluates algorithm performance, robustness, convergence, and sensitivity to hyperparameters. Hyperparameter Tun...
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.