Update README.md

README.md

@@ -35,23 +35,43 @@ The data generation process itself was orchestrated using the [Inductiva API](ht
 which allowed us to run hundreds of OpenFOAM simulations in parallel on the cloud. 
 
 
-# Why?
-Existing object datasets have many limitations: they are either small in size, closed source, or have low quality meshes. 
-Hence, we decided to generate a new dataset using the [InstantMesh](https://github.com/TencentARC/InstantMesh) model, 
-which is open-source (Apache-2.0) and is currently state-of-the-art in image-to-mesh generation.
-
-The automobile-like meshes were generated by running the image-to-mesh model [InstantMesh](https://github.com/TencentARC/InstantMesh) 
-on 1k images from the publicly available (Apache-2.0)
-[Stanford Cars Dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-cars-dataset) consisting of 16,185 images of automobiles.
-
-Naturally, running the image-to-mesh model leads to meshes that may have certain defects, such as irregular surfaces, asymmetry issues
-and disconnected components. Therefore, after running the image-to-mesh model, we run a custom post-processing step where we try to
-improve the meshes quality. We used PCA to align the mesh with the main axis and we removed disconnected components. 
-
-The resulting set of meshes still have little defects, such as presence of "spikes" or "cavities" in supposedly flat areas and
-asymmetric shapes, among others. We consider these little defects as valuable features of the dataset not as issues, since from the
-point of view of the learning problem, they bring challenges to the model that we believe will contribute to obtaining more robust
-and generalizable models. 
+# Motivation
+Recently, there has been great interest in developing
+ML methods to accelerate CFD simulations. Research
+has shown that for well defined CFD simulation scenarios (e.g. a virtual wind tunnel), it is possible to train an ML model capable of “predicting” the end result of the simulation orders of magnitude faster than existing classical simulation methods, while maintaining comparable accuracy levels.
+
+However, the ML/CFD communities still lack training data
+for their research. We identified two main reasons for that.
+
+First, there is a fundamental lack of datasets containing
+3D meshes over which we can run CDF simulation. In fact existing  3D object datasets have many limitations: they are either small in size, closed source, or have low quality meshes. The absence of such input data has been a fundamental blocker for any attempt to generate large-scale training data in any realistic CFD scenario, which will naturally involve 3D meshes.
+
+Second, even if you had all the 3D meshes you needed, it is still not trivial to orchestrate the thousands CFD
+simulations that are required to generate a large and diverse enough dataset for training ML-based CFD methods. For creating such a dataset one has to be
+able to define an initial simulation scenario (e.g. the windtunnel scenario), and run enough
+variations of the simulation, with different meshes, different wind speeds, etc to cover a wide enough range of data points to train a generalizable and robust ML model. Now, using most CFD software, running one simulation alone may be difficult enough. Orchestrating thousands of them and managing all the resulting data is a challenge in itself.
+
+While both of these problems are difficult to solve in general, we decided to focus on one common CFD scenario: a virtual wind tunnel for (static) automobiles and produce a large dataset of CFD simulation run using the popular simulation package OpenFOAM.
+
+Next, we will explain how we tackled the data and the simulation orchestration issues.
+
+
+## Generating a large quantity of Automobile-like 3D Meshes
+Due to the lack of publicly available 3D meshes of automobile objects, we decided to use recent advances in image-to-mesh models to generate meshes from images of automobiles that are freely available. 
+
+More specifically, we used the InstantMesh model, which is open-source (Apache-2.0) and is currently state-of-the-art in image-to-mesh generation. The automobile-like meshes were generated by running the InstatMesh image-to-mesh model on 1k images from the publicly available (Apache-2.0) Stanford Cars Dataset consisting of 16,185 images of automobiles.
+
+Naturally, running the image-to-mesh model leads to meshes that may have certain defects, such as irregular surfaces, asymmetry issues, holes and disconnected components. Therefore, after running the image-to-mesh model, we run a custom post-processing step where we try to improve the meshes quality. We used PCA to align the mesh with the main axis and we removed disconnected components.
+
+The resulting set of meshes still have little defects, such as presence of "spikes" or "cavities" in supposedly flat areas, unexpected holes, asymmetry issues, among others. We consider these defects as valuable features of the dataset, since from the point of view of the learning problem, they bring certain challenges to ML models that we believe will make overfitting harder and will generally contribute to obtaining more robust and generalizable models.
+
+
+## Orchestrating 20k simulations on the cloud (just using Python)
+For solving the challenge of orchestrating 20k OpenFOAM simulations, we resorted to the Inductiva API. The Inductiva platform exposes a simple Python API for running simulation workflows on the cloud. Inductiva makes available several popular open-source simulation packages, including OpenFOAM. Here is an example of how to run an OpenFOAM simulation using Inductiva (point to the doc that Paulo is preparing). 
+
+Using the Inductiva API, it becomes easy to parametrise specific simulation scenarios and run variations of a base case by programatically changing the input parameters and starting conditions of the simulation. Additionally, users can build custom Python classes that wrap parameterized simulation scenarios, allowing them to have a simple Python interface to running simulations without the need to directly interface with the low level simulation packages. We leveraged Inductiva API to create a Python class for the Wind Tunnel scenario (point to GitHub), which we then used to run 20k simulations over a range of input parameters.
+
+For more information on how to transform complex simulation workflows in simple Python classes check this blog post (point to the blog post).
 
 
 # How did we generate the dataset?