MindSpore

Installation

  • MindSpore Elec Introduction and Installation

Application

  • Physics Informed Deep Learning Method for Electromagnetic Simulation
  • Data Driven Deep Learning Method for Electromagnetic Simulation
  • Device-to-device differentiable FDTD method
  • Visualizing Electromagnetic Simulation Results

API References

  • mindelec.architecture
  • mindelec.common
  • mindelec.data
  • mindelec.geometry
  • mindelec.loss
  • mindelec.operators
  • mindelec.solver
  • mindelec.vision

RELEASE NOTES

  • Release Notes
MindSpore
  • »
  • Overview: module code

All modules for which code is available

  • mindelec.architecture.activation
  • mindelec.architecture.basic_block
  • mindelec.architecture.mtl_weighted_loss
  • mindelec.common.lr_scheduler
  • mindelec.common.metrics
  • mindelec.data.boundary
  • mindelec.data.data_base
  • mindelec.data.dataset
  • mindelec.data.equation
  • mindelec.data.existed_data
  • mindelec.data.pointcloud.pointcloud
  • mindelec.geometry.csg
  • mindelec.geometry.geometry_1d
  • mindelec.geometry.geometry_2d
  • mindelec.geometry.geometry_3d
  • mindelec.geometry.geometry_base
  • mindelec.geometry.geometry_nd
  • mindelec.geometry.geometry_td
  • mindelec.geometry.utils
  • mindelec.loss.constraints
  • mindelec.loss.losses
  • mindelec.loss.net_with_loss
  • mindelec.operators.derivatives
  • mindelec.solver.callback
  • mindelec.solver.problem
  • mindelec.solver.solver
  • mindelec.vision.body
  • mindelec.vision.mindinsight_vision
  • mindelec.vision.plane
  • mindelec.vision.print_scatter
  • mindelec.vision.video

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