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https://github.com/usarmyresearchlab/arl-hierarchical-multiscale-framework

The ARL Hierarchical MultiScale Framework (ARL-HMS) is a software library for development of multiscale models on heterogeneous high-performance computing systems.
https://github.com/usarmyresearchlab/arl-hierarchical-multiscale-framework

adaptive-computation multiscale-modeling scale-bridging

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The ARL Hierarchical MultiScale Framework (ARL-HMS) is a software library for development of multiscale models on heterogeneous high-performance computing systems.

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README 

        
## ARL Hierarchical MultiScale Framework (ARL-HMS)



The ARL Hierarchical MultiScale Framework (ARL-HMS) is a software

library for development of multiscale models on heterogeneous

high-performance computing systems. Multiscale models are created in

ARL-HMS from an assembly of individual at-scale model components. A

runtime system adaptively evaluates at-scale model components in

parallel and performs data extraction necessary for

scale-bridging. Adaptive machine learning methods are also available

in ARL-HMS to significantly reduce computational expense.



The ARL-HMS software is implemented in C++ and includes bindings to

Python. It can incorporate individual at-scale model components

written in any programming language and can also incorporate

closed-source or proprietary software allowing users to leverage

existing complex at-scale models for multiscale model development.



A User's Guide to ARL-HMS is included in this software repository

(ARL-TR-9820.pdf). The User's Guide documents the steps necessary to

develop multiscale models with ARL-HMS. It includes an overview of the

mathematical approach to multiscale modeling the framework adopts, the

steps necessary to incorporate at-scale models, use of the ARL-HMS

Broker to perform at-scale model evaluation and data extraction, and

use of the adaptive surrogate modeling capabilities implemented in

ARL-HMS. A simple example ARL-HMS application, implemented in both C++

and Python, is included that performs Monte Carlo estimation of

$\pi$. The example application is also included in the repository in

the examples/mcpi directory.



### ARL-HMS Dependencies and Compilation Steps



ARL-HMS requires a compiled Boost C++ installation for data

serialization and shared pointer capabilities.



Optional dependencies include a Message Passing Interface (MPI)

library to enable MPI communication and fan-in data communication

between ARL-HMS components. A Python installation is required to build

the ARL-HMS Python bindings. The Adaptive Sampling Framework (ASF)

library from LLNL (https://github.com/exmatex/aspa) is also required

to use the Kriging database interpolation method.



ARL-HMS uses a standard autotools installation procedure (configure,

make, make install) documented in Section 5 of the ARL-HMS User's

Guide.



### ARL-HMS Citations



We request that if you publish results that make use of the ARL-HMS

software, please cite the following paper and ARL-HMS User's Guide:



```

@article{Knap2016,

  author = {Knap, J. and Spear, C. and Leiter, K. and Becker, R. and Powell, D.},

  title = {A computational framework for scale-bridging in multi-scale simulations},

  journal = {International Journal for Numerical Methods in Engineering},

  volume = {108},

  number = {13},

  pages = {1649-1666},

  doi = {https://doi.org/10.1002/nme.5270},

  year = {2016}

}

```

```

@techreport{Leiter2023,

  institution = {{DEVCOM} Army Research Laboratory},

  title={User's Guide for the {Hierarchical MultiScale Framework} ({HMS})},

  author={Leiter, Kenneth W and Crone, Joshua C and Knap, Jaroslaw},

  year={2023},

  month={Oct},

  number={ARL-TR-9820}

}

```

### License



ARL Hierarchical MultiScale Framework (ARL-HMS) is licensed under the

Creative Commons Zero 1.0 Universal (CC0 1.0) license. Please see

[LICENSE](LICENSE) for details.