Call for abstract submission to WCCM/PANACM 2024: MS1815 Machine learning algorithms for accelerating material characterization, discovery, design, and manufacturing processes

Dear Colleagues, 

We cordially invite you to submit an abstract to our Mini-Symposium: MS1815, "Machine learning algorithms for accelerating material characterization, discovery, design, and manufacturing processes," for the 16th World Congress on Computational Mechanics (WCCM-PANACM 2024) held on July 21-26, 2024, in Vancouver, BC, Canada. 

The deadline for submission of Abstracts is January 15, 2024. 

For more information on WCCM-PANACM 2024, please visit the conference website: https://www.wccm2024.org/. 

For guidelines for the submission of abstracts, as well as further information regarding the conference, may be found at the website https://www.wccm2024.org/abstract-submission

Summary of Mini-symposium:

With the synthesis of new high-throughput methods, materials R&D is readying for the discovery, characterization, and design of robust materials and manufacturing processes through the development and implementation of machine learning algorithms spanning multimodality, physics constraints, Gaussian processes, and causal inference. The fusion of human expert materials knowledge with multimodal, physically constrained machine learning algorithms can aid in the detection of "fingerprints" critical in materials behavior, prognose component performance, and adapt manufacturing strategies. 

This mini-symposium convenes world-class researchers in advanced manufacturing, materials characterization, data science, modeling/simulation, and hardware engineering to showcase works with the ability to further materials discovery, characterization, and design. Researchers from national labs, academia, and industry will present and discuss topics such as hybrid, physics—informed machine learning methods to understand process-structure-property mappings, surrogate models using multimodal data streams combining experiments and simulations, and machine learning-guided process optimization. 

Co-organizers:

Jonas Actor, Troy Shilt, Ankit Shrivastava, and Elise Walker

Sandia National Laboratories