From deae4025b86e8e3d58505b07feb24230523b8744 Mon Sep 17 00:00:00 2001 From: Ishaan Desai Date: Mon, 16 Sep 2024 16:08:39 +0200 Subject: [PATCH] Add info about adaptivity tuning parameters --- docs/configuration.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/configuration.md b/docs/configuration.md index e7fe0b7..afeb006 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -121,6 +121,12 @@ Parameter | Description `every_implicit_iteration` | If True, adaptivity is calculated in every implicit iteration.
If False, adaptivity is calculated once at the start of the time window and then reused in every implicit time iteration. `similarity_measure`| Similarity measure to be used for adaptivity. Can be either `L1`, `L2`, `L1rel` or `L2rel`. By default, `L1` is used. The `rel` variants calculate the respective relative norms. This parameter is *optional*. +The primary tuning parameters for adaptivity are the history parameter $$ \Lambda $$, the coarsening constant $$ C_c $$, and the $$ C_r $$. Their effects can be interpreted as: + +- Higher values of the history parameter $$ \Lambda $$ imply lower significance of the adaptivity state in the previous timestep on the state in the current timestep. +- Higher values of the coarsening constant $$ C_c $$ imply that more active simulations from the previous timestep will remain active in the current timestep. +- Higher values of the refining constant $$ C_r $$ imply that less inactive points from the previous timestep will become active in the current timestep. + Example of adaptivity configuration is ```json