Feb. 12, 2019 — The HPC4EnergyInnovation (HPC4EI) Program will host an online technical colloquium on Machine Learning. The online colloquium is designed to introduce machine learning concepts; show examples of applications of machine learning tools; and discuss potential pitfalls in applying these tools. The HPC4Manufacturing and HPC4Materials programs within the HPC4EnergyInnovation Program are currently executing projects that combine machine learning tools with physics-based simulation tools and/or sensor-based data for both process and product enhancement. The agenda for the colloquium is included below.

Date: March 22, 2019

12:00 p.m. EST/9:00 a.m. PST

HPC4EnergyInnovation Program Overview: National Laboratories Partner with U.S. Manufacturers to Increase Innovation and Energy Efficiency
Robin Miles, HPC4EI Program Director

12:15 p.m. EST/9:15 a.m. PST

Motivation for Machine Learning in Product and Process Development
David Womble, Oak Ridge National Laboratory

12:30 p.m. EST/9:30 a.m. PST

What Can Deep Learning Do for You?
Brenda Ng, Lawrence Livermore National Laboratory

1:30 p.m. EST/10:30a.m. PST

Modern Data Analytics Approach to Predict Creep of High-Temperature Alloys
Dongwon Shin, Oak Ridge National Laboratory

2:00 p.m. EST/11:00 a.m. PST
Machine Learning for Material Property Design at the Atomic Level
Tess Smidt, Lawrence Berkeley National Laboratory

2:30 p.m. EST/11:30 a.m. PST
Machine Learning for Better Understanding and Control of Complex Processes
Victor Castillo, Lawrence Livermore National Laboratory

3:00 p.m. EST/12:00 p.m. PST
Error Analysis of System Modeling using Artificial Intelligence and Machine Learning
Brian Valentine, Department of Energy, EERE, AMO

3:30 p.m. EST/12:30 p.m. PST
Accelerated Search for Materials with Targeted Properties
Turab Lookman, Los Alamos National Laboratory

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Source: HPC4EnergyInnovation