- Msepostdoc-list - ecn.purdue.edu

Seminar : June 17, 2021 : 2:00 PM : George Warren
by Noerenberg, Catherine L 07 Jun '21

07 Jun '21

Please consider attending this seminar: [cid:image003.jpg@01D75BBA.9BD8F290]<https://engineering.purdue.edu/MSE/events/Thesis-Defenses/thesis-defense-ge…> Web Link<https://engineering.purdue.edu/MSE/events/Thesis-Defenses/thesis-defense-ge…> Cathy Noerenberg Materials Engineering Neil Armstrong Hall of Engineering 701 West Stadium Avenue, Room 2200 West Lafayette, IN 47907

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Seminar : June 14, 2021 : 9:30 AM : Cole Davis
by Noerenberg, Catherine L 02 Jun '21

02 Jun '21

Please consider attending this seminar: [cid:image002.jpg@01D757A4.BE3EF8D0]<https://engineering.purdue.edu/MSE/events/Thesis-Defenses/thesis-defense-co…> Web Link<https://engineering.purdue.edu/MSE/events/Thesis-Defenses/thesis-defense-co…> Cathy Noerenberg Materials Engineering Neil Armstrong Hall of Engineering 701 West Stadium Avenue, Room 2200 West Lafayette, IN 47907

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Seminar : June 15, 2021 : 0:30 AM : Xin Li Phuah
by Noerenberg, Catherine L 02 Jun '21

02 Jun '21

Please consider attending this seminar: [cid:image003.jpg@01D757A4.BCBF0F90]<https://engineering.purdue.edu/MSE/events/Thesis-Defenses/thesis-defense-xi…> Web Link<https://engineering.purdue.edu/MSE/events/Thesis-Defenses/thesis-defense-xi…> Cathy Noerenberg Materials Engineering Neil Armstrong Hall of Engineering 701 West Stadium Avenue, Room 2200 West Lafayette, IN 47907

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Still time to register to today’s and next week's hands-on machine learning workshop using nanoHUB
by Ku Blanco, Aury Y 26 May '21

26 May '21

Dear Colleagues, A few available seats remain for the first two workshops in the Summer 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. Title: A Hands-on Introduction to Physics-Informed Neural Networks Date: May 26th 2021, 1:30 PM - 2:30 PM EST Speaker: Ilias Bilionis, Purdue University Register here (limited seats): https://nanohub.org/groups/ml/handsontraining Abstract: Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential equations. I will focus on differential equations in this short presentation. The simplest way to bake information about a differential equation with neural networks is to create a regularization term for the loss function used in training. I will explain the mathematics of this idea. I will also talk about applying physics-informed neural networks to a plethora of applications spanning the range from solving differential equations for all possible parameters in one sweep (e.g., solve for all boundary conditions) to calibrating differential equations using data to design optimization. Then, we will work on a hands-on activity that shows you to implement the ideas in PyTorch. I am assuming some familiarity with how conventional neural networks are trained (stochastic gradient descent). Also, you need to know the basics of PyTorch to follow along. Going over this tutorial should be sufficient: https://pytorch.org/tutorials/beginner/pytorch_with_examples.html. Title: Batch Reification Fusion Optimization (BAREFOOT) Framework Date: June 2nd 2021, 1:30 PM - 2:30 PM EST Speaker: Richard Couperthwaite, Texas A&M University Register here (limited seats): https://nanohub.org/groups/ml/handsontraining Abstract: This tutorial will present the fundamentals of multi-fidelity fusion as well as Sequential and Batch Bayesian Optimization as possible optimization approaches that can be integrated with high accuracy computational models or experimental procedures to speed up the optimization or design of materials. The tutorial will also provide an overview of the BAREFOOT framework that combines these two approaches together to further improve the rate of the optimization. All these approaches are compared using the optimization of the workability of a dual-phase steel microstructure. Alejandro Strachan Deputy Director, nanoHUB (2020 R&D 100 winner) -- Alejandro Strachan Professor of Materials Engineering, Purdue University Network for Computational Nanotechnology - nanohub.org<http://nanohub.org> Center for Predictive Materials and Devices (c-PRIMED)

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Fall Grad Course – Tech commercialization and entrepreneurship
by Noerenberg, Catherine L 25 May '21

25 May '21

___________________________________________________________________________ Prepare for careers in academia or industry. Attached please find a flyer describing a course that will be offered again in Fall 2021. Life of a Faculty Entrepreneur: Discovery, Development, Translation (ABE 62600/TLI 62600). Date/Time: Tuesdays from 4:00 – 6:40pm in new Agriculture and Biological Engineering Building, Room 1164. For more information about the course, please contact Mike Ladisch at Ladisch(a)purdue.edu<mailto:Ladisch@purdue.edu> or Nathalie Duval-Couetil at natduval(a)purdue.edu<mailto:natduval@purdue.edu>. [cid:image002.png@01D7515C.F65B0400] ////////////////////////////////////////////////////// Nathalie Duval-Couetil, MBA, PHD Professor, Technology, Leadership, and Innovation Director, Certificate in Entrepreneurship and Innovation Program Associate Director, Burton D. Morgan Center for Entrepreneurship Purdue University Office: 765-494-7068 Cell: 765-337-4687 Email: natduval(a)purdue.edu<mailto:natduval@purdue.edu>

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Reminder: Seminar : May 24, 2021 : 10:00 AM : Ryan Deskins
by Noerenberg, Catherine L 23 May '21

23 May '21

Please consider attending this seminar: [cid:image001.jpg@01D74D6C.307CA200]<https://purdue.webex.com/meet/aelazab> Web Link<https://engineering.purdue.edu/MSE/events/Thesis-Defenses/thesis-defense-ry…> Cathy Noerenberg Materials Engineering Neil Armstrong Hall of Engineering 701 West Stadium Avenue, Room 2200 West Lafayette, IN 47907

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Hands-on, free, workshops on machine learning May 23 & 1:30 PM EDT - registration open (limited seats)
by Ku Blanco, Aury Y 21 May '21

21 May '21

Dear Colleagues, nanoHUB is excited to announce the first two workshops in the Summer 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 participants 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. Title: A Hands-on Introduction to Physics-Informed Neural Networks Date: May 26th 2021, 1:30 PM - 2:30 PM EST Speaker: Ilias Bilionis, Purdue University Register here (limited seats): https://nanohub.org/groups/ml/handsontraining Abstract: Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential equations. I will focus on differential equations in this short presentation. The simplest way to bake information about a differential equation with neural networks is to create a regularization term for the loss function used in training. I will explain the mathematics of this idea. I will also talk about applying physics-informed neural networks to a plethora of applications spanning the range from solving differential equations for all possible parameters in one sweep (e.g., solve for all boundary conditions) to calibrating differential equations using data to design optimization. Then, we will work on a hands-on activity that shows you to implement the ideas in PyTorch. I am assuming some familiarity with how conventional neural networks are trained (stochastic gradient descent). Also, you need to know the basics of PyTorch to follow along. Going over this tutorial should be sufficient: https://pytorch.org/tutorials/beginner/pytorch_with_examples.html. Title: Batch Reification Fusion Optimization (BAREFOOT) Framework Date: June 2nd 2021, 1:30 PM - 2:30 PM EST Speaker: Richard Couperthwaite, Texas A&M University Register here (limited seats): https://nanohub.org/groups/ml/handsontraining Abstract: This tutorial will present the fundamentals of multi-fidelity fusion as well as Sequential and Batch Bayesian Optimization as possible optimization approaches that can be integrated with high accuracy computational models or experimental procedures to speed up the optimization or design of materials. The tutorial will also provide an overview of the BAREFOOT framework that combines these two approaches together to further improve the rate of the optimization. All these approaches are compared using the optimization of the workability of a dual-phase steel microstructure. Alejandro Strachan Deputy Director, nanoHUB (2020 R&D 100 winner) -- Alejandro Strachan Professor of Materials Engineering, Purdue University Network for Computational Nanotechnology - nanohub.org<http://nanohub.org> Center for Predictive Materials and Devices (c-PRIMED)

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Seminar : May 24, 2021 : 10:00 AM : Ryan Deskins
by Noerenberg, Catherine L 20 May '21

20 May '21

Please consider attending this seminar: [cid:image003.jpg@01D74D6B.F9C585B0]<https://purdue.webex.com/meet/aelazab> Web Link<https://engineering.purdue.edu/MSE/events/Thesis-Defenses/thesis-defense-ry…> Cathy Noerenberg Materials Engineering Neil Armstrong Hall of Engineering 701 West Stadium Avenue, Room 2200 West Lafayette, IN 47907

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Missing MSE Plastic Cart
by Stacey, Lisa A 10 May '21

10 May '21

Greetings, if you borrowed one of the black plastic 3-tier carts from MSE 2312 copy room, please return it as soon as possible, we need to use it. Phil, please let me know if someone left it in 1128, should be labeled 'Property of MSE'. Thank you, 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-…>

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Hands-on workshops of machine learning May 12th and 19th, Register Now
by Stacey, Lisa A 07 May '21

07 May '21

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-…> ________________________________ Subject: Hands-on, free, workshop on machine learning May 12th and May 19th, 2021 at 1:30 PM EDT - registration open (limited seats) Dear Colleagues, nanoHUB is excited to announce the nest two workshops in 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: May 12th 2021, 1:30 PM - 2:30 PM EST Title: An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties Speaker: Benjamin Afflerbach, University of Wisconsin-Madison Register here (limited seats): https://nanohub.org/groups/ml/handsontraining Abstract. This workshop will introduce core concepts of machine learning through the lens of a basic workflow to predict material bandgaps from material compositions. As we progress through this workflow, we’ll highlight key steps, challenges that can come up with materials data, and potential solutions to these challenges. The core workflow we’ll introduce includes: Data Cleaning, Feature Generation, Feature Engineering, Establishing Model Assessment, Training a Default Model, Hyperparameter Optimization, and Making Predictions. By the end of the workshop I hope that you’ll have a better understanding of these core concepts, and how they can all fit together. If you want to preview the materials ahead of time you can find them on nanoHUB here: https://nanohub.org/tools/intromllab. Date: May 19th 2021, 1:30 PM - 2:30 PM EST Title: Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction Speaker: Ryan Jacobs, University of Wisconsin-Madison Register here (limited seats): https://nanohub.org/groups/ml/handsontraining Abstract. This tutorial contains an introduction to the use of the Materials Simulation Toolkit for Machine Learning (MAST-ML), a python package designed to broaden and accelerate the use of machine learning and data science methods for materials property prediction. Through hands-on activities, we will use MAST-ML to (1) import materials datasets from online databases and clean and examine our input data, (2) conduct feature engineering analysis, including generation, preprocessing, and selection of features, (3) construct, evaluate and compare the performance of different model types and data splitting techniques, and (4) conduct a preliminary assessment of model error analysis and uncertainty quantification (UQ). MAST-ML code: https://github.com/uw-cmg/MAST-ML Publication: https://doi.org/10.1016/j.commatsci.2020.109544 MAST-ML tutorials: https://github.com/uw-cmg/MAST-ML/tree/master/examples Best, -- Alejandro Strachan Professor of Materials Engineering, Purdue University Network for Computational Nanotechnology - nanohub.org<http://nanohub.org> Center for Predictive Materials and Devices (c-PRIMED)

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