Exploring scientific machine learning pipelines through the SimulAI toolkit
<p>SciML, short for Scientific Machine Learning, encompasses work that merges quantitative sciences with machine learning. It has gained significant traction over the past decade, driven by the widespread availability of specialized hardware (such as GPUs and TPUs) and datasets. Additionally, it has been propelled by the overarching influence of the machine learning wave, now ingrained in the zeitgeist of our times. In this context, we’d like to introduce <a href="https://github.com/IBM/simulai" rel="noopener ugc nofollow" target="_blank">SimulAI</a>, an open-source toolkit under the Apache 2.0 license. SimulAI is designed to be user-friendly, providing a high-level Python interface for managing scientific machine learning pipelines. This article aims to showcase its current workflow and utility in constructing scientific experiments. We encourage feedback and potential contributions from the interested community, with plans to delve into more advanced topics in future articles.</p>
<p><a href="https://medium.com/pytorch/exploring-scientific-machine-learning-pipelines-through-the-simulai-toolkit-9fda42d6c6a0"><strong>Read More</strong></a></p>