Forwarding on behalf of Ale Strachan: Lisa Stacey Administrative Assistant to the Department Head School of Materials Engineering Neil Armstrong Hall of Engineering o: 765-494-4095 f: 765-494-1204 [3749DD84]<https://urldefense.proofpoint.com/v2/url?u=https-3A__www.purdue.edu_-3Futm-5Fsource-3Dsignature-26utm-5Fmedium-3Demail-26utm-5Fcampaign-3Dpurdue&d=DwMFAg&c=l45AxH-kUV29SRQusp9vYR0n1GycN4_2jInuKy6zbqQ&r=cYZgnbkB39R9SYPCvL_MkXuih1H-IjjKyRBtQtQ6ib8&m=H5ec4nXIY6Mt-FgrmXhcB1ClcZZRWvtSm3BzPca31UE&s=ra1N_tE4A3L7IxFqllrao41XR8k0ynZZYRxh3KMpTwY&e=> ________________________________ Subject: Hands-on, free, workshop on machine learning April 23 1:30 PM EDT - registration open (limited seats) Dear Colleagues, nanoHUB is excited to announce the first four workshops for the Spring 2021 session of our Hands-on Data Science and Machine Learning Training Series. Series information. Our series is aimed at active researchers and educators and designed to introduce practical skills with online, hands-on activities participnts will be able to incorporate in their work. Hands-on activities will use nanoHUB cloud computing resources, no need to download or install any software. All you need is an internet connection and a browser. After the training sessions, you will be able to continue using nanoHUB for research or education. Registration links and material for prior workshops can be found at the workshop webpage: https://nanohub.org/groups/ml/handsontraining Register soon as seats are limited. Date/Time: Friday, April 23, 2021 / 1:30 PM – 2:30 PM EDT Title: Parsimonious Neural Networks Learn Interpretable Physical Laws By: Saaketh Desai, Sandia National Labs Register here (limited seats): https://nanohub.org/groups/ml/handsontraining Abstract: Machine learning methods are widely used as surrogate models in the physical sciences, but less explored is the use of machine learning to discover interpretable laws from data. This tutorial introduces parsimonious neural networks (PNNs), a combination of neural networks and evolutionary optimization to find models that balance accuracy with parsimony. As an example, you will learn how to train a PNN to learn interpretable laws that predict the melting temperature of a material given fundamental properties such as elastic constants and volume. You will also learn how to interpret the discovered PNN models as physical laws, and understand how various PNN models, as well as traditional models such as the Lindemann melting law, trade parsimony and accuracy. Alejandro Strachan Deputy Director, nanoHUB (2020 R&D 100 winner)
