From a11c14082ffeda3f56f1e2bd5a1f43e109c9a665 Mon Sep 17 00:00:00 2001 From: Gennadii Zakharov Date: Sat, 23 Nov 2024 10:35:51 +0000 Subject: [PATCH] GROK-17011: Wiki: Compute: Fixed duplicated section names. --- .../advanced-scripting/rich-function-view.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/help/compute/scripting/advanced-scripting/rich-function-view.md b/help/compute/scripting/advanced-scripting/rich-function-view.md index 8aa013b27f..adde6da98d 100644 --- a/help/compute/scripting/advanced-scripting/rich-function-view.md +++ b/help/compute/scripting/advanced-scripting/rich-function-view.md @@ -16,8 +16,8 @@ It has all the features of the [basic scripting](../scripting-features/scripting * [Review historical script runs](#review-and-compare-historical-script-runs) * [Upload external data](#upload-external-data) * [Provide custom docs and export](#provide-custom-docs-and-export-data) -* Use powerful [parameter optimization](#parameter-optimization) capabilities with your script - without a single new line of the code. +* Use [model parameters optimization](#model-parameters-optimization) capabilities with your script + without a single line of the code. * Use [helper JavaScript functions](js-helpers-with-rich-fucntion-view.md) to customize RichFucntionView behavior. :::caution Package dependency @@ -450,7 +450,7 @@ Click on the column header with table data (e.g. `Temp. vs time`) to see conveni ``` -## Parameter optimization +## Model parameters optimization With **RichFunctionView** you can use the powerful built-in optimization functions. @@ -531,10 +531,11 @@ simulation = DG.DataFrame.fromColumns([ ``` -### Parameter optimization +### Parameters fitting -[Parameter optimization](../../function-analysis.md#sensitivity-analysis) -solves an inverse problem: finding the input conditions that lead to a specified output of the model. +The parameters fitting solves an inverse problem to the +[sensitivity analysis](../../function-analysis.md#sensitivity-analysis): +finding the input conditions that lead to a specified output of the model. It computes inputs minimizing deviation measured by loss function. :::warning High-intensity computation