BEGIN:VCALENDAR
VERSION:2.0
PRODID:OpenCms 21.0.27
BEGIN:VTIMEZONE
TZID:Europe/Berlin
X-LIC-LOCATION:Europe/Berlin
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700329T020000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701025T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20230424T183315
UID:5b9c7c49-3ec3-11ef-b437-000e0c3db68b
SUMMARY:SimTech Colloquium: Scientific Machine Learning Models for Microstructure Texture Prediction in Metal Additive Manufacturing
DESCRIPTION:Date: 3 May 2023Place: https://unistuttgart.webex.com/unistuttgart/j.php?MTID=ma4f926f72a0d25102f3905ae9dba6788Time: 4pm\nIn this lecture series, leading researchers and promising young scientists report on their latest findings in the field of simulation science. We place particular emphasis on the interdisciplinary variety of contributions. We invite all interested parties to our colloquium.\nScientific Machine Learning Models for Microstructure Texture Prediction in Metal Additive ManufacturingPredicting grain formation during alloy solidification is of great importance in additive manufacturing (AM). Numerical simulations require fine spatial and temporal discretizations that can be computationally expensive. &nbsp;In this talk, I will &nbsp;discuss GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN &nbsp;is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. We present results in which GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. This speedup includes the cost of the phase field simulations for generating training data. GrainNN enables predictive simulations and uncertainty quantification of grain microstructure for situations not previously possible.This is joint work with Yigong Qin (UT Austin), Steve DeWitt (ORNL), and Balasubramanian Radhakrishnan (ORNL)\nCVGeorge Biros is the W. A. ``Tex'' Moncrief Chair in Simulation-Based Engineering Sciences in the Oden Institute for Computational Engineering and Sciences and has Full Professor appointments with the departments of Mechanical Engineering and Computer Science (by courtesy) at The University of Texas at Austin. From 2008 to 2011, he was an Associate Professor in the School of Computational Science and Engineering at Georgia Tech and The Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. From 2003 to 2008, he was an Assistant professor in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. &nbsp;He received his BS in Mechanical Engineering from Aristotle University in Greece (1995), his MS in Biomedical Engineering from Carnegie Mellon (1996), and his PhD in Computational Science and Engineering also from Carnegie Mellon (2000). &nbsp;He was a postdoctoral associate at the Courant Institute of Mathematical Sciences from 2000 to 2003. With collaborators, he received the ACM Gordon Bell Prize in 2003 and in 2010.&nbsp;
DTSTART;TZID=Europe/Berlin:20230503T160000
URL;VALUE=URI:https://www.simtech.uni-stuttgart.de/news/events/SimTech-Colloquium-Scientific-Machine-Learning-Models-for-Microstructure-Texture-Prediction-in-Metal-Additive-Manufacturing-00001/
END:VEVENT
END:VCALENDAR
