Infrared.city has established a strong academic foundation through extensive peer-reviewed publications and conference contributions at leading venues. These works cover a broad spectrum of topics including AI-driven performance-based design, resilient urban planning, deep-learning approaches to environmental simulation, and optimization methods for wind, solar, and climate adaptation. By consistently advancing the integration of computational design, machine learning, and environmental performance feedback, infrared.city and its collaborators have contributed to shaping the global discourse on sustainable and intelligent design processes.

Canuto, R., Koenig, R., Chronis, A., Galanos, T., Celani, G. (2024). From performative to predictive-performative design: A review of current trends in performance-based design and their impact on urbanism. International Journal of Architectural Computing, 2024;23(1):307-330. Link

The paper outlines Predictive-Performative Design (PPD), an AI-driven shift in performance-based design, and its impact on urban planning, drawing on literature and case studies from MIT and AIT. It also highlights future research directions in this emerging field.

Chronis, A., Aichinger, A., Düring, S. B., Galanos, T., Fink, T., Vesely, O., & Koenig, R. (2020). INFRARED An Intelligent Framework for Resilient Design. Proceedings of the 25th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2020. Bangkok. Link

The paper introduces INFRARED, a framework that combines parametric design, machine learning, and augmented reality to deliver real-time environmental performance feedback, supporting resilient and sustainable design decisions.

Chronis, A. (2021). PROFILE: An Intelligent Framework for Resilient Design (InFraReD). In Hyde, R. and Filippidis, F. (eds). Intelligent Control: Disruptive Technologies. Link

The paper introduces INFRARED, a framework that combines parametric design, machine learning, and augmented reality to deliver real-time environmental performance feedback, supporting resilient and sustainable design decisions.

Chronis, A., Galanos, T., Düring, S. B., & Khean, N. (2022). InFraReD - Accessible Environmental Simulations. in S. Chaillou (eds). Artificial Intelligence and Architecture - From Research to Practice, 180-187. Link

This paper introduces InFraReD, an intelligent framework that combines machine learning with large simulation datasets to deliver near real-time environmental performance feedback. By making simulations more accessible, it supports architects and planners in integrating resilient design decisions into their workflows.

Düring, S. B., Fink, T. R., Chronis, A., & Koenig, R. (2022). Environmental Performance Assessment - The Optimisation of High-Rises in Vienna. POST-CARBON - Proceedings of the 27th CAADRIA Conference on Computer Aided Architectural Design Research in Asia (CAADRIA), 1, 545–554. Link

This paper presents a framework that uses automated model generation and machine learning to optimize high-rise building performance. A Vienna case study shows how design space exploration accelerates simulations and supports sustainable planning.

Düring, S. B., Koenig, R., Khean, N., Elshani, D., Galanos, T., & Chronis, A. (2022). Machine Learning, Artificial Intelligence, and Urban Assemblages. Machine Learning and the City, 445–452. Wiley. Link

This chapter showcases generative methods for urban design with InFraRed, enabling large-scale exploration through simulation and machine learning. It emphasizes cognitive design computing, linking performance evaluation with design configuration.

Düring, S., Chronis, A., Koenig, R. (2020). Optimizing Urban Systems: Integrated optimization of spatial configurations. Proceedings of the Symposium on Simulation for Architecture and Urban Design (SimAUD 2020), 503-509. Vienna. Link

This study shows how machine learning and generative design reduce barriers in early-stage simulations. A Vienna case study optimizes spatial layouts for multiple objectives and supports manual design processes.

Elshani, D., Koenig, R., Duering, S., Schneider, S., & Chronis, A. (2021). Measuring sustainability and urban data operationalization: An integrated computational framework to evaluate and interpret the performance of the urban form. Proceedings of the 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2021, 407–416. Link

This paper presents a computational framework combining generative design, simulation, and machine learning to evaluate urban form performance. It enables design space exploration and supports participatory planning with interactive tools.

Galanos, T. and Chronis, A. (2021). A deep-learning approach to real-time solar radiation prediction. In As, I., and Basu, P. (eds). The Routledge Companion to Artificial Intelligence in Architecture, 1-9. Link

This paper proposes a deep learning approach to predict environmental simulations, enabling real-time performance feedback in early design stages. Accurate solar radiation predictions demonstrate its potential as part of an intelligent framework for resilient, performance-driven design.

Galanos, T. and Chronis, A. (2022). Time for Change – The InFraRed Revolution: How AI-driven Tools can Reinvent Design for Everyone. Architectural Design, 92(3), 108–115. Link

This paper describes AI tools developed to provide real-time, dynamic representations of design performance and intent, blending human creativity with computational methods.

Giraud, I., Tudzh­arova, V., Erdi, A., Chronis, A., Düring, S. B., Fink, T. R., & Khean, N. (2021). TREEHOPPER A tool development for identifying potential areas for roadside tree planting. Responsive Cities Symposium 2021, 296-306. Link

This research presents TreeHopper, a web platform using data analysis and augmented reality to support participatory tree planting. It engages citizens and authorities in inclusive, data-informed planning with environmental and social benefits.

Hamann, S. M., Chronis, A., Taut, O., & Galanos, T. (2025). Advanced weather data morphing for future climate-based building simulation: A modular Python tool utilizing enhanced morphing algorithms for EPW file generation. Proceedings of the Annual Modeling and Simulation Conference (ANNSIM), Madrid, Spain, 2025, 1-13. Link

This paper presents a Python-based tool for generating future weather files using enhanced morphing algorithms. Tested with Vienna data under SSP3-7.0, it projects changes in temperature, precipitation, and solar radiation, advancing climate-based building simulation for resilient design.

Kabošová, L., Chronis, A., Galanos, T., & Katunský, D. (2021). Leveraging Urban Configurations for Achieving Wind Comfort in Cities. Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics, SIGraDi 2021, 79–90. Link

This paper introduces a design method that integrates wind analysis tools, including infrared.city, into early-stage urban design. A case study shows how iterative simulations help identify design alternatives that significantly improve outdoor wind comfort.

Kabošová, L., Chronis, A., Galanos, T., Kmeť, S., & Katunský, D. (2022). Shape optimization during design for improving outdoor wind comfort and solar radiation in cities. Building and Environment, 226. Link

This paper presents a weather-based optimization method combining solar and wind analysis with AI-driven simulations. A Košice case study shows how iterative evaluation improves wind comfort and sunlight access.

Kabošová, L., Chronis, A. and Galanos, T. (2022). Fast wind prediction incorporated in urban city planning. International Journal of Architectural Computing, 20(3), 511–527. Link

This paper introduces a wind prediction–based design method using parametric modeling and InFraRed analysis to improve outdoor comfort. A Košice case study demonstrates efficient evaluation of urban design options.

Khean, N., Düring, S. B., Chronis, A., Koenig, R., & Haeusler, M. H. (2022). An Assessment of Tool Interoperability and its Effect on Technological Uptake for Urban Microclimate Prediction with Deep Learning Models. Proceedings of the 27th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA 2022), 1, 273–282. Link

This paper applies deep learning to urban microclimate simulations, emphasizing deployment for practical use. It introduces a Grasshopper plugin that predicts wind conditions efficiently, cutting costs and supporting resilient design.

Sampathraj, K. L., Fragkia, V., & Chronis, A. (2025). Assessing urban wind environments: A design optimization framework. Proceedings of the 2025 Annual Modeling and Simulation Conference (ANNSIM 2025), 1–9. IEEE. Link

This paper presents a framework for early-stage urban wind assessment that balances geometric, environmental, and design factors. A case study demonstrates how the method supports optimization and practical integration of wind analysis into urban planning and building design.