9 citations · 10 across the 4 of their papers we have counts for
8 papers
StationRank: Aggregate dynamics of the Swiss railway
Georg Anagnostopoulos, Vahid Moosavi
Increasing availability and quality of actual, as opposed to scheduled, open transport data offers new possibilities for capturing the spatiotemporal dynamics of the railway and ot…
Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
Zifeng Guo, Joao P. Leitao, Nuno E. Simoes +1
Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding an…
Modeling overland flow from local inflows in almost no-time, using Self Organizing Maps
Joao P. Leitao, Mohamed Zaghloul, Vahid Moosavi
Physically-based overland flow models are computationally demanding, hindering their use for real-time applications. Therefore, the development of fast (and reasonably accurate) ov…
Data-Driven Design: Exploring new Structural Forms using Machine Learning and Graphic Statics
Lukas Fuhrimann, Vahid Moosavi, Patrick Ole Ohlbrock +1
The aim of this research is to introduce a novel structural design process that allows architects and engineers to extend their typical design space horizon and thereby promoting t…
Urban morphology meets deep learning: Exploring urban forms in one million cities, town and villages across the planet
Vahid Moosavi
Study of urban form is an important area of research in urban planning/design that contributes to our understanding of how cities function and evolve. However, classical approaches…
Computational Machines in a Coexistence with Concrete Universals and Data Streams
Vahid Moosavi
We discuss that how the majority of traditional modeling approaches are following the idealism point of view in scientific modeling, which follow the set theoretical notions of mod…