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Webinar: Introduction of Multiscale Analysis Techniques to Accelerate PCB Design

Submitted by Chandima Uyanage on

Many types of composites are used in Printed Circuit Boards (PCB). Virtual Material Testing can help to find optimal materials properties to match various design requirements resulting in a faster design process and increased product life.

Save the date: 30 November 2022 at 11 AM CET ⇒ Register from here

Webinar Abstract

Webinar on Undergraduate Research Opportunities in STEM

Submitted by John E. Dolbow on

Happy to announce that along with Oscar Lopez-Pamies from UIUC, I will be hosting a webinar this Friday 11/4 at noon Eastern time.  The webinar will discuss undergraduate research opportunities in STEM.  It will be held over Zoom but also live-streamed via Facebook.

Interested students or faculty can register here:

https://us06web.zoom.us/webinar/register/WN_jGIHjz9RR8ycX4JiErO3nA

Ansys Webinar - FEM Analysis for Compaction Process of Powders

Submitted by Chandima Uyanage on

I would like to invite you to sign up for this upcoming materials webinar on 15 November 2022 (11 AM EST / 4 PM GMT / 9:30 PM IST) to see the press powder process analysis realized by co-simulation of Ansys® Rocky, Ansys LS-DYNA®, and Multiscale.Sim™ ⇒ Register from here

MIT Short Course: Machine Learning for Materials Informatics (Live Virtual, Sept. 26-29, 2022)

Submitted by Markus J. Buehler on

In this course you will fully learn how to incorporate new materials informatics methods into your own material modeling, analysis and design processes in order to capitalize on recent AI breakthroughs, such as language models (e.g. GPT-3, BERT, LaMDA, etc.), DNA and protein models (e.g., AlphaFold), graph neural networks applied from molecular to macroscale structures, and a host of methods adapted for computer vision including diffusion models (as used in DALL-E 2 or Imagen), specifically for the analysis, design and modeling of materials. The course involves a mix of lectures, hands-on labs and clinics for an immersive experience. Participants will learn fundamentals and techniques to deploy machine learning in materials development and gain first-hand understanding of state-of-the art tools for varied applications ranging from data mining to inverse design. We will cover scales from the molecular to the continuum.