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Gradient flow based phase-field modeling using separable neural networks
Revanth Mattey
, Susanta Ghosh
Nuclear Fuels & Materials
Research output
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Contribution to journal
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Article
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peer-review
4
Scopus citations
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Dive into the research topics of 'Gradient flow based phase-field modeling using separable neural networks'. Together they form a unique fingerprint.
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Keyphrases
Neural Network
100%
Phase-field Model
100%
Flow-based
100%
Gradient Flow
100%
Allen-Cahn Equation
100%
Minimizing Movement Scheme
100%
Machine Learning Techniques
75%
Flow Problems
50%
Phase Field
50%
Collocation Technique
50%
Order of Magnitude
25%
Finite Element Method
25%
Partial Differential Equations
25%
Phase Separation
25%
System Size
25%
Free Energy Functional
25%
Large Systems
25%
Energy Stability
25%
Strong Form
25%
Hyperbolic Tangent
25%
Spatial Derivatives
25%
Computational Implementation
25%
L2-gradient Flow
25%
Gauss Quadrature
25%
Minimizing Movements
25%
Higher Order Spatial Derivative
25%
Reaction-diffusion Equations
25%
Low-rank Tensor Decomposition
25%
Speed Improvement
25%
Engineering
Phase Field
100%
Flow Problem
66%
Machine Learning Method
66%
Spacetime
66%
Finite Element Analysis
33%
Illustrates
33%
Applicability
33%
Partial Differential Equation
33%
Phase Separation
33%
Energy Functional
33%
Machine Learning Technique
33%
Strong Form
33%
Hyperbolic Tangent
33%
Gaussian Quadrature Rule
33%
Rank Tensor
33%
Mathematics
Neural Network
100%
Phase Field
100%
Gradient Flow
100%
Spatial Derivative
50%
Finite Element Method
25%
Partial Differential Equation
25%
Energy Functional
25%
Rank Tensor
25%
Strong Form
25%
Tensor Decomposition
25%
System Size
25%
Chemical Engineering
Neural Network
100%
Learning System
100%
Free Energy
33%
Phase Separation
33%
Material Science
Finite Element Method
100%