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Next generation fire models provide the basis to understand fire physics in detail, leading the way for emulators to model potential fire behavior. This project uses data from hundreds of coupled fire-atmosphere simulations using the QUIC-Fire model to develop a reduced-order emulator. Such an emulator can be used to predict the wildfire spread, which can help the fire agencies to take necessary steps to reduce the damage. Additionally, the predictions can be utilized to mitigate the risk of controlled fires escalating into wildfires. The project encompasses several objectives: developing a system to evaluate the performance of deep learning models through benchmarking, conducting the benchmarking process, and leveraging the obtained results to enhance the accuracy of the models. Our product utilizes cloud infrastructure to provide a scalable and robust solution for managing the entire lifecycle of machine learning. This infrastructure enabled us to conduct numerous experiments, analyze results, and optimize various deep learning models, including U_Net, TF_Net,PhyDNet, and ConvLSTM. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Gurvich, Sergey; Perera, Camm; Dwivedi, Brij; Subramanian, Solaimalai; Munipalli, Sirish; Altintas De Callafon, Ilkay; Perez, Ismael; Yu, Rose (2023). Fire Science Physics Informed Machine Learning. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0FX79NP
Type
dataset
Identifier
ark:/20775/bb9362558h
Language
English
Subject
Machine learning Physics-guided machine learning Wildfire spread prediction Physics-informed machine learning Capstone projects Deep learning Neural networks (Computer science) Data Science & Engineering Master of Advanced Study (DSE MAS) Kubernetes Physics guided machine learning (PGML) DSE MAS - 2023 Cohort
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