some doc improvements

Author: chmerdon

docs/src/estimators.md

@@ -1,6 +1,6 @@
 # A posteriori error estimation
 
-## Spatial refinement
+## Residual-based error estimation
 Spatial error estimation refers to classical residual-based error estimation
 for the zero-th multi index that refers to the mean value.
 Stochastic error control has to estimate which stochastic mode needs to be refined

docs/src/estimators.pdf

@@ -5,7 +5,7 @@ model problems involving KLE of stochastic coefficients. The rather huge systems
 structure and are solved by iterative solvers. A posteriori error estimators steer the spatial
 and stochastic refinement.
 
-The spatial discretization is based on the finite element package [ExtendableFEM.jl](https://github.com/WIAS-PDELib/ExtendableFEM.jl) and [ExtendableFEMBase.jl](https://github.com/WIAS-PDELib/ExtendableFEMBase.jl)
+The spatial discretization is based on the finite element package [ExtendableFEM.jl](https://github.com/WIAS-PDELib/ExtendableFEM.jl)/[ExtendableFEMBase.jl](https://github.com/WIAS-PDELib/ExtendableFEMBase.jl)
 
 
 ## References

docs/src/index.md

@@ -40,15 +40,14 @@ $(TYPEDSIGNATURES)
 
 computes the residual-based a posteriori error estimator for the current solution vector `sol`
 for the given model problem and stochastic coefficient `C` and returns:
-- eta4modes = array with total error estimators for each multi-index (corresponding to the enriched set of multi-indices
-with the current active modes coming first)
-- eta4cell = array with total error estimators for each cell in grid (for spatial refinement)
-- multi_indices_extended = enriched set of multi-indices used for the computations
-- f4modes
+- `eta4modes` = array with total error estimators for each multi-index (corresponding to the enriched set of multi-indices with the current active modes coming first)
+- `eta4cell` = array with total error estimators for each cell in grid (for spatial refinement)
+- `multi_indices_extended = enriched set of multi-indices used for the computations
 
 """
 function estimate(::Type{AbstractModelProblem}, sol::SGFEVector, C::AbstractStochasticCoefficient; kwargs...)
-    return @error "no error estimator for the model problem type available"
+    @error "no error estimator for the model problem type available"
+    return Vector{Float64}, Matrix{Float64}, [[0]]
 end