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Neural Network Enhanced RKPM for Electrochemical-Mechanical Coupled Damage Modeling of Energy Storage Materials
Kristen Susuki,
Jeffrey Allen
, J. Chen
Computational Science
National Renewable Energy Laboratory
University of California, San Diego
Research output
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NREL
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Dive into the research topics of 'Neural Network Enhanced RKPM for Electrochemical-Mechanical Coupled Damage Modeling of Energy Storage Materials'. Together they form a unique fingerprint.
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Engineering
Anisotropic
100%
Energy Storage
100%
Reproducing Kernel
66%
Material Interface
33%
Crack Propagation
33%
Lithium Ion Battery
33%
Fickian Diffusion
33%
Material Microstructure
33%
Crack Opening
33%
Mechanical Coupling
33%
Mechanical Deformation
33%
Grain Material
33%
Mechanical Problem
33%
Consistency Condition
33%
Deformation Field
33%
Boundary Condition
33%
Grain Boundaries
33%
Service Life
33%
Material Science
Lithium
100%
Energy Storage Material
100%
Particle Method
50%
Materials Property
50%
Lithium Ion Battery
25%
Interface (Material)
25%
Mechanical Deformation
25%
Anisotropic Material
25%
Crack Propagation
25%
Grain Boundaries
25%