Cburgers

The purpose of this mini-application is to demonstrate how one may deploy scientific machine learning within a computational physics workflow. We claim that this code represents a *practical* deployment because it satisfies the following features: 1. The computation is performed using a compiled language as is the case with most legacy codes (C++). 2. We avoid disk-IO through in-situ transfer of data from the numerical computation to the machine learning computation (in Python). In addition, this code also highlights the advantages of integrating the Python ecosystem with C++. We now have the following capabilities: 1. Utilizing arbitrary data science libraries such as TensorFlow through their Python APIs. 2. Easy in-situ visualization in matplotlib from a C++ computation. 3. A potential interface (if there are no issues with security) to streaming data from the internet (from say, a Python API). 4. Easy ability to save data using formats like HDF5 or NetCDF4. The test-case demonstrated here is representative of several Sci-ML workloads. We aim to build a surrogate model using TensorFlow in Python from data generated by a C++ computation. The methodology we utilize is something called the "POD-LSTM" - here snapshots of the solution field are linearly compressed using an SVD and the compressed representations are used as training data within a long short-term memory (LSTM) neural network. The LSTM is used to forecast compressed representations of the solution field in the future (for more details please visit our Editor's pick article [here](https://doi.org/10.1063/5.0019884)). However, this educational proxy-app may easily be modified to solve more complex problems (for example: closure modeling, data assimilation, and control) which assess the interplay of compute and ML.

Argonne National Laboratory

DOE/ASCR

OpenFOAM/NekRS/E3SM/TensorFlow/Numpy

Machine learning/Computational Physics

C++/Python

https://github.com/Romit-Maulik/CBurgers

0.1

The purpose of this mini-application is to demonstrate how one may deploy scientific machine learning within a computational physics workflow. We claim that this code represents a *practical* deployment because it satisfies the following features: 1. The computation is performed using a compiled language as is the case with most legacy codes (C++). 2. We avoid disk-IO through in-situ transfer of data from the numerical computation to the machine learning computation (in Python). In addition, this code also highlights the advantages of integrating the Python ecosystem with C++. We now have the following capabilities: 1. Utilizing arbitrary data science libraries such as TensorFlow through their Python APIs. 2. Easy in-situ visualization in matplotlib from a C++ computation. 3. A potential interface (if there are no issues with security) to streaming data from the internet (from say, a Python API). 4. Easy ability to save data using formats like HDF5 or NetCDF4. The test-case demonstrated here is representative of several Sci-ML workloads. We aim to build a surrogate model using TensorFlow in Python from data generated by a C++ computation. The methodology we utilize is something called the "POD-LSTM" - here snapshots of the solution field are linearly compressed using an SVD and the compressed representations are used as training data within a long short-term memory (LSTM) neural network. The LSTM is used to forecast compressed representations of the solution field in the future (for more details please visit our Editor's pick article [here](https://doi.org/10.1063/5.0019884)). However, this educational proxy-app may easily be modified to solve more complex problems (for example: closure modeling, data assimilation, and control) which assess the interplay of compute and ML.