February 12, 2019 – The HPC4Energy Innovation Program (HPC4EI) will host an online technical colloquium on machine learning. The online colloquium will introduce concepts for machine learning. Show examples of machine learning tools applications; and discuss possible pitfalls in using these tools. The HPC4Manufacturing and HPC4Materials programs within the HPC4EnergyInnovation program are currently running projects that combine machine-learning tools with physics-based simulation tools and / or sensor-based data for process and product enhancements. The agenda for the colloquium is listed below.

Date: March 22, 2019

12:00. EST / 9: 00h PST

Overview of the HPC4Energy Innovation Program: National Laboratories Collaborates with US Manufacturers to Increase Innovation and Energy Efficiency
Robin Miles, program director for HPC4EI

12:15 pm EST / 9:15 pm PST

Motivation for machine learning in product and process development
David Womble, National Oak Ridge Laboratory

12:30 pm. EST / 9:30 am PST

What can deep learning do for you?
Brenda Ng, Lawrence Livermore National Laboratory

1:30 pm EST / 10:30 am. PST

Modern data analysis method for predicting the creep behavior of high-temperature alloys
Dongwon Shin, National Oak Ridge Laboratory

2:00 pm EST / 11:00 am PST
Machine learning for the design of material properties at the atomic level
Tess Smidt, Lawrence Berkeley National Laboratory

2.30. EST / 11:30 am PST
Machine learning to better understand and control complex processes
Victor Castillo, Lawrence Livermore National Laboratory

3:00 pm EST / 12:00 pm PST
Error analysis of system modeling with artificial intelligence and machine learning
Brian Valentine, Department of Energy, EERE, AMO

3:30 in the afternoon. EST / 12:30 pm PST
Accelerated search for materials with targeted properties
Turab Lookman, National Laboratory Los Alamos


Source: HPC4EnergyInnovation

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