From 76291a37b9b88f90fa6401c9a1ca93e7b12fae66 Mon Sep 17 00:00:00 2001 From: Anna Roubickova <38880646+aroubickova@users.noreply.github.com> Date: Wed, 1 Nov 2023 22:36:40 +0000 Subject: [PATCH] Update README.md 0s -> zeroes --- docs/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/README.md b/docs/README.md index 1084a54..ef6855e 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,4 +1,4 @@ -**SiMLInt is an [ExCALIBUR](https://excalibur.ac.uk/) project demonstrating how to integrate Machine Learning (ML) to physics simulations. It combines commonly used, open-source tools with in-house Python scripts to execute ML-aided computational fluid dynamics simulations.** +f**SiMLInt is an [ExCALIBUR](https://excalibur.ac.uk/) project demonstrating how to integrate Machine Learning (ML) to physics simulations. It combines commonly used, open-source tools with in-house Python scripts to execute ML-aided computational fluid dynamics simulations.** ## Codes and Dependencies @@ -35,7 +35,7 @@ We demonstrate the workflow on the Hasegawa-Wakatani set of equations using a du ## Model training -The example workflow uses a model that returns always 0s for the correction, maintaining the simulation on the same trajectory it would follow without any ML adjustments. LC, however, requires a model that is trained to predict the difference between the fully resolved trajectory that runs over a sufficiently fine resolution of the domain and a trajectory that uses coarser domain decomposition (and coarser time steps). To train such a ML model, we need to generate data matching this scenario, as detailed below. +The example workflow uses a model that returns always zeroes for the correction, maintaining the simulation on the same trajectory it would follow without any ML adjustments. LC, however, requires a model that is trained to predict the difference between the fully resolved trajectory that runs over a sufficiently fine resolution of the domain and a trajectory that uses coarser domain decomposition (and coarser time steps). To train such a ML model, we need to generate data matching this scenario, as detailed below. The training dataset can be generated as follows: 1. Run a fully resolved simulation (denoted F)