A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety
type: Item
instance of: scholarly article
title: A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety
zbMATH Open document ID: 1506.76028
author: Tarek I. Zohdi
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2021-04-26T00:00:00Z
Mathematics Subject Classification ID: 76D07
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 70B10
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 7337742
zbMATH Keywords: aerial fire-fighting
zbMATH Keywords: fire retardants
zbMATH Keywords: optimization
zbMATH Keywords: machine-learning
describes a project that uses: FARSITE
describes a project that uses: FlamMap
MaRDI profile type: Publication
full work available at URL: j.cma.2020.113446
OpenAlex ID: W3091823377
cites work: A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments
cites work: Modeling the spatio-thermal fire hazard distribution of incandescent material ejecta in manufacturing
cites work: Computation of strongly coupled multifield interaction in particle-fluid systems
cites work: On the dynamics of charged electromagnetic particulate jets
cites work: Q3873786
cites work: Mechanistic modeling of swarms
cites work: Q4079017
cites work: Q4692508
cites work: Special issue. Genetic and evolutionary computation
cites work: A combined penalty function and gradient projection method for nonlinear programming
cites work: Advances in the particle finite element method for the analysis of fluid-multibody interaction and bed erosion in free surface flows
cites work: Possibilities of the particle finite element method for fluid-soil-structure interaction problems
cites work: High-density sphere packing for discrete element method simulations
DOI: 10.1016/J.CMA.2020.113446
description: scientific article; zbMATH DE number 7337742