RAR

Class which is used to implement residule-based adaptive refinement

RAR

Bases: adaptives

Class which implements stategy to call Residule-based Adaptive Refinement, based on DeepXDE: A deep learning library for solving differential equations

__init__(frequency, pointsperfreq)

Parameters:
  • frequency (int) –

    How many epochs between sampling new collocation points.

  • pointsperfreq (int) –

    How many points to add in each sample.

get_frequency()

Returns:
  • int –

    How many epochs between sampling new collocation points.

get_pointsperfreq()

Returns:
  • int –

    How many points to add in each sample.

AdaptiveStrategy(model, domain, data, clps, ds_data, ds, i)

Sampling stategy to call Residule-based Adaptive Refinement.

Parameters:
  • model (network) –

    Tensorflow network.

  • domain (domain) –

    Domain class solving over.

  • data (data) –

    Data class solving with.

  • clps (tensor) –

    Current collocation points.

  • ds_data (list) –

    Data being packaged in training routine.

  • ds (list) –

    Current ds value of training routine.

  • i (int) –

    Iteration number.