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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.

Maybe what I know about mechanics, especially statistic mechanics, is limited. But I am wondering that whether is the some kind of research like "on the constitutive modeling for elastomer with machine learning" interesting and encouragable?




Machine learning includes a large set of techniques that can be summarized as curve fitting in high dimensional spaces.  Mechanicians have used these techniques, such as neural networks or genetic algorithms, extensively over the years without calling them "machine learning".  The recent resurgence in interest in machine learning comes form the development for new algorithms (in particular, significant improvements in neural net algorithms) and the availablity of large amounts of data which make the curve fits much more accurate.  The usefulness of the new  techniques should not be underestimated.

One application domain that comes to mind in improving the speed of detailed mechanics computations (say FEA) by fitting nets instead of redoing the same calculations over and over at each Gauss point.  For other approaches see

-- Biswajit

Thanks, Biswajit. I am agree with you. My first impression of mechanicians using machine learning is its applications in empirical modeling. However, the part of FEA quite interests me and I'm going to search for some examples.

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