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Getting Started
Getting Started with Meent
Tutorials
Modeling and Electromagnetic simulation
Gradient and Optimization with JAX and Optax
Gradient and Optimization with PyTorch
Device Setting - CPU or GPU in JAX
Device Setting - CPU and GPU in PyTorch
Background Theory
Electromagnetic Simulation
Geometry
Fourier Analysis
Eigenmodes Identification
Connecting Layers
Enhanced Transmittance Matrix Method
Machine Learning
User Guides
Program Sequence
Benchmarks
Public API: meent package
meent.on_numpy package
meent.on_numpy.emsolver package
meent.on_numpy.modeler package
meent.on_jax package
meent.on_jax.emsolver package
meent.on_jax.modeler package
meent.on_jax.optimizer package
meent.on_torch package
meent.on_torch.emsolver package
meent.on_torch.modeler package
meent.on_torch.optimizer package
Applications
Applications
Beam Deflector Optimization
Color Router
Fourier Neural Operator
Model-based RL
Indices and tables
Index
Module Index
Search Page
.rst
.pdf
meent.on_jax.modeler package
Contents
Submodules
meent.on_jax.modeler.modeling module
Module contents
meent.on_jax.modeler package
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Submodules
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meent.on_jax.modeler.modeling module
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Module contents
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Contents
Submodules
meent.on_jax.modeler.modeling module
Module contents
