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APL Photonics • Vol 8 • No 3
Exploiting graph neural networks to perform finite-difference time-domain based optical simulations
February 2023 • Lukas Kuhn, Taavi Repän, Carsten Rockstuhl
Having an artificial neural network that solves Maxwell’s equations in a general setting is an intellectual challenge and a great utility. Recently, there have been multiple successful attempts to use artificial neural networks to predict electromagnetic fields, given a specific source and interacting material distribution. However, many of these attempts are limited in domain size and restricted to object shapes similar to the learned ones. Here, we overcome these restrictions by using graph neural networks (GNNs…
Finite-Difference Time-Domain Method
Polygon Mesh
Computer Science
Electromagnetic Field
Convolutional Neural Network
Theoretical Computer Science
Benchmark (Surveying)
Algorithm
Electromagnetism
Artificial Intelligence
Mathematics
Mathematical Analysis
Physics
Optics
Computer Graphics
Quantum Mechanics
Geography
Geodesy