rom: LSPG: steady masked problem

Parameters and Requirements

• fom_adapter:
  • instance of your adapter class specifying the FOM problem.
    
  • must satisfy the steady API
• decoder:
  • decoder object
  • must satify the requirements listed here
• rom_state:
  • currently, must be a rank-1 numpy.array
• fom_ref_state:
  • your FOM reference state that is used when reconstructing the FOM state
  • must be a rank-1 numpy.array
• masker:
  • functor responsible of "masking" the FOM operators

  • must be a functor with a specific API:

    class Masker:
      def __init__(self, sample_indices):
        self.sample_indices = sample_indices
        self.N = len(self.sample_indices)
    
      def createApplyMaskResult(self, operand):
          if (operand.ndim == 1):
            return np.zeros(N)
          else:
            return np.zeros((N, , operand.shape[1]))
    
      def __call__(self, operand, result):
        result[:] = np.take(operand, self.sample_indices)

     
• preconditioner:
  • functor needed to precondition the ROM operators

  • must be a functor with a specific API:

    class Prec:
      def __call__(self, fom_state, operand):
        # given the current FOM state,
        # apply your preconditioner to the operand.
        # Ensure that you overwrite the data in the operand.
        # As an example, a trivial preconditioner that does nothing:
        # operand[:] *= 1.