1 citations · 1 across the 5 of their papers we have counts for
6 papers
NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling
Ting-Yu Dai, Takuya Kurihana, Wing Yee Au +1
Demand-side flexibility i.e. forecasting, shifting, and curtailing residential energy loads, depends on thermal models trusted across millions of heterogeneous buildings. Existing…
Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model
Ion Matei, Maksym Zhenirovskyy, Takuya Kurihana +4
Aerial wildfire suppression requires decisions about when, where, and how to deploy limited aircraft. We present an intervention-design framework built on a frozen hybrid convoluti…
Neural-Parameterized Cellular Automata for Wildfire Spread
Maksym Zhenirovskyy, Ion Matei, Rohit Vuppala +2
Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this weakness, we introduce a hybrid…
Harnessing Rich Multi-Modal Data for Spatial-Temporal Homophily-Embedded Graph Learning Across Domains and Localities
Takuya Kurihana, Xiaojian Zhang, Wing Yee Au +1
Modern cities are increasingly reliant on data-driven insights to support decision making in areas such as transportation, public safety and environmental impact. However, city-lev…
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman +4
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the…
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models
Lijing Wang, Takuya Kurihana, Aurelien Meray +6
Soil and groundwater contamination is a pervasive problem at thousands of locations across the world. Contaminated sites often require decades to remediate or to monitor natural at…