PHYSICS-INFORMED NEURAL NETWORK FOR INVERSELY PREDICTING EFFECTIVE MATERIAL PROPERTIES OF METAMATERIALS

PHYSICS-INFORMED NEURAL NETWORK FOR INVERSELY PREDICTING EFFECTIVE MATERIAL PROPERTIES OF METAMATERIALS

Present application provides systems and method implement apply a Physics-Informed Neural Network (PINN) for inversely calculating the effective material parameters of a multi-dimensional metamaterial from its scattered field(s). By employing a loss function based on the Helmholtz wave equation, per...

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Bibliographische Detailangaben
Hauptverfasser: PILLAI, PRAJITH, PAL, PARAMA, RAI, BEENA, CHAUDHURI, ANIRBAN
Format: Patent
Sprache:eng ; fre ; ger
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Beschreibung
Zusammenfassung:Present application provides systems and method implement apply a Physics-Informed Neural Network (PINN) for inversely calculating the effective material parameters of a multi-dimensional metamaterial from its scattered field(s). By employing a loss function based on the Helmholtz wave equation, performance of a metamaterial is modeled by the system the dependance of resonant behavior on the homogenized electric permittivity distribution profile generated by the PINN is demonstrated.