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Research

Our research spans from environmental modeling and digital twins to generative AI, synthetic data and agent-centric modeling with a focus on robotics.

Wildfire simulations with detailed 3D plant models

​By combining computer graphics and artificial intelligence, we aim to develop mathematical models aiming to represent, understand, and predict the processes and dynamics of the environment. Our research focuses on creating geometric representations and mathematical models of environmental systems to explore the interactions within these systems, with the following directions:

  • Scale and Scope: Environmental models can vary greatly in scale, from local (e.g., a small ecosystem or urban area) to global (e.g., climate models). The scope can also vary and focus on specific processes (such as a forest fire) or complex interactions within entire ecosystems.

  • Types of Models: Models can be categorized into several types based on their approach and complexity, including physical (based on physical laws), empirical (based on observational data), and mechanistic models (based on the mechanisms underlying environmental processes). Hybrid models combine these approaches to leverage their strengths.

  • Data and Calibration: Environmental modeling heavily relies on data for development, calibration, and validation. These data can come from a variety of sources, including remote sensing, ground-based observations, and historical records. Advanced models also integrate real-time data feeds to improve accuracy and relevance.

  • Simulation and Prediction: A core application area of environmental models is simulating scenarios and predicting future states under various conditions. This includes simulating the impacts of climate change, urban development, pollution, and conservation strategies, among many others.

Selected Publications: 

A. Kokosza, H. Wrede, D. G. Esparza, M. Makowski, D. Liu, D. L. Michels, S. Pirk, W. Pałubicki, Scintilla: Simulating Combustible Vegetation for Wildfires, ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2024

[Website], [Preprint], [Video], [Bibtex]

J. A. Amador Herrera, J. Klein, D. Liu, W. Pałubicki, S. Pirk, Dominik, L. Michels, Cyclogenesis: Simulating Hurricanes and Tornadoes, ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2024

[Website], [Preprint], [Video], [Bibtex]

W. Pałubicki, M. Makowski, W. Gajda, T. Hädrich, D. L. Michels, S. Pirk, Ecoclimates: Climate-Response Modeling of Vegetation, ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2022

[Website], [Preprint], [Video], [Bibtex]

T. Niese, S. Pirk, M. Albrecht, B. Benes, O. Deussen, Procedural Urban Forestry, ACM Transactions on Graphics (TOG) and SIGGRAPH, 2022[Website], [Preprint], [Video], [Bibtex]

J. A. Amador Herrera, T. Hädrich, W. Pałubicki, D. T. Banuti, S. Pirk, D. L. Michels, Weatherscapes: Nowcasting Heat Transfer and Water Continuity, ACM Transactions on Graphics (SIGGRAPH Asia), 2021

[Website], [Preprint], [Video], [Technical Papers Trailer], [Bibtex]

T. Hädrich, D. T. Banuti, W. Pałubicki, S. Pirk, D. L. Michels, Fire in Paradise: Mesoscale Simulation of Wildfires, ACM Transactions on Graphics (SIGGRAPH), 2021

[Website], [Preprint], [Video], [Talk], [Two Minute Papers], [Gallery of Fluid Motion], [Bibtex]

M. Makowski, T. Hädrich, J. Scheffczyk, D. L. Michels, S. Pirk, W. Pałubicki, Synthetic Silviculture: Multi-scale Modeling of Plant Ecosystems, ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2019
[Website], [Preprint], [Video], [Technical Papers Trailer], [Two Minute Papers], [Bibtex]

 

S. Pirk, T. Niese, T. Hädrich, B. Benes, O. Deussen, Windy Trees: Computing Stress Response for Developmental Tree Models, ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia), 2014
[Website], [Preprint], [DOI], [Video], [Bibtex]

Kiel University
Department of Computer Science   
Visual Computing and Artificial Intelligence
Neufeldtstraße 6 (Ground Floor)
D-24118 Kiel
Germany

 © Visual Computing and Artificial Intelligence Group 2025

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