machine learning

MS27: Computational GeomechanicsWaiChing Sun, Columbia University
Jose Andrade, Caltech
Ronaldo Borja, Stanford University
Jinhyun Choo, University of Hong Kong
Majid Manzari, George Washington University
Richard Regueiro, University of Colorado Boulder

Title: Modeling and Simulation Engineer

Category: Full-time position in industry

Employer: Schlumberger Technology Corporation

Location: United States, Texas, Sugar Land

Opening Date: 12/01/2017

Job Description

Dear colleagues,

We encourage you to submit your abstracts to minisymposium 1801 of the 13th World Congress on Computational Mechanics (New York City, from July 22 to 27 of 2018). This minisymposium focuses on:

1. recently developed methods for data-driven approaches;

2. data-driven applications to fluids, structures and materials involving (but not limited to) machine learning, uncertainty quantification and/or optimization.

Hi all,

I  know that the study and application of machine learning/artificial intelligence are somehow popular nowadays, in a general academia. I happen to be interested in that. I found people use this tool to predict crystal structure in material science, to characterize molecule structure in chemistry. The first impression is that the machanince learning helps to deal with the raw data, to constitute empirical models etc. However, there are not as many published works as I imagine, on the application of machine learning in mechanics.

A new perspective on model reduction for nonlinear multi-scale analysis of heterogeneous materials. In this work, we seek meaningful low-dimensional structures hidden in high-dimensional multi-scale data.