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mbessa's picture

[June 5 | USNC/TAM2018] Short-course on data-driven modeling in mechanics and materials science

Dear colleagues in academia and industry,

We are organizing the first short course on data-driven computational mechanics and materials science at the 18th US National Congress on Theoretical and Applied Mechanics (USNC/TAM) in Chicago on June 5th, 2018. See description of short-course SC007 in the following website:

WaiChing Sun's picture

Last Call for Abstracts: EMI mini-symposium on Computational Geomechanics, Boston 5/29-6/1/2018 (abstracts due tomorrow)

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

Haitao Zhang's picture

Full-Time Position in Schlumberger – Modeling and Simulation Engineer


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

mbessa's picture

MS1801 @ WCCM2018 -> Data-driven Methods and Applications: from Physics-informed Learning Machines to Optimization Under Uncertainty

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.

Is machine learning a research priority now in mechanics?

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.

karelmatous's picture

A nonlinear manifold-based reduced order model

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.

karelmatous's picture

Predictive Multiscale Materials Modelling

Workshop on Predictive Multiscale Materials Modelling was held at the Isaac Newton Institute for Mathematical Sciences, University of Cambridge UK. Slides and videos of all presentations can be seen at the workshop website

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