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MOPINNs : an evolutionary multi-objective approach to ph..:

, In: Proceedings of the Genetic and Evolutionary Computation Conference Companion,

de Wolff, Taco ; Lincopi, Hugo Carrillo ; Martí, Luis. - p. 228-231 , 2022

Link: https://dl.acm.org/doi/10.1145/3520304.3529071

RT 
T1 <cTitle>Proceedings of the Genetic and Evolutionary Computation Conference Companion</cTitle>: T1 <Title>MOPINNs				: an evolutionary multi-objective approach to physics-informed neural networks</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3529071&Exemplar=1&LAN=DE
				
A1 de Wolff, Taco
A1 Lincopi, Hugo Carrillo
A1 Martí, Luis
A1 Sanchez-Pi, Nayat
PB ACM
YR 2022
K1 evolutionary algorithms
K1 multi-objective optimization (MOO)
K1 physics-informed neural networks (PINNs)
K1 weighted PINNs
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Neural networks
K1 Bio-inspired approaches
K1 Genetic algorithms
K1 Applied computing
K1 Physical sciences and engineering
K1 Earth and atmospheric sciences
K1 Environmental sciences
K1 Modeling and simulation
K1 Model development and analysis
K1 Modeling methodologies
K1 Mathematics of computing
K1 Mathematical analysis
K1 Differential equations
K1 Partial differential equations
K1 Learning paradigms
K1 Multi-task learning
SP 228
OP 231
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3520304.3529071
DO https://dl.acm.org/doi/10.1145/3520304.3529071
SF ELIB - SuUB Bremen

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