169 citations · 200 across the 4 of their papers we have counts for
6 papers
Interpretable inverse design of particle spectral emissivity using machine learning
Mahmoud Elzouka, Charles Yang, Adrian Albert +2
We examine the optical properties of a system of nano and micro particles of varying size, shape, and material (including metals and dielectrics, and sub-wavelength and super-wavel…
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
Rui Wang, Karthik Kashinath, Mustafa Mustafa +2
While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, t…
Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks
Adrian Albert, Jasleen Kaur, Emanuele Strano +1
Accurately forecasting urban development and its environmental and climate impacts critically depends on realistic models of the spatial structure of the built environment, and of…
Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems
Jin-Long Wu, Karthik Kashinath, Adrian Albert +3
Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully…
Modeling urbanization patterns with generative adversarial networks
Adrian Albert, Emanuele Strano, Jasleen Kaur +1
In this study we propose a new method to simulate hyper-realistic urban patterns using Generative Adversarial Networks trained with a global urban land-use inventory. We generated…
Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale
Adrian Albert, Jasleen Kaur, Marta Gonzalez
Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount…