EML Special Issue Call for Paper: Machine Learning and Mechanics

Extreme Mechanics Letters (EML) Special Issue: Call for Paper 

Machine Learning and Mechanics

Over the past decade, there has been a growing interest in applying machine learning techniques to problems in mechanics. From material design optimization, manufacturing, to multiscale modelling, to real time prediction and autonomy, data mining and analysis, machine learning techniques have had a massive impact in the field.

Currently, machine learning methods are accessible to researchers across the broad range of mechanics sub-disciplines. Thus, the aim of this special issue is to highlight recent advances across this diverse field – both in terms of fundamental methodology and applications – and set the tone for the next generation of machine learning in mechanics research. Now that these techniques have emerged as ubiquitous tools, what new research directions in mechanics are possible, what new developments in computational methods and AI strategies are critical, and what impacts can this have on solving critical challenges that could not be tackled previously?

Guest editors:

Markus Buehler

Massachusetts Institute of Technology, Cambridge, Massachusetts, United States

(Multiscale Mechanics; Scientific AI; Generative and De Novo Design; Bio-Inspired Materials; Materials Failure)

Grace Gu

University of California Berkeley, Berkeley, California, United States

(AI for Manufacturing; Architected Materials; Generative Design)

Emma Lejeune

Boston University, Boston, Massachusetts, United States

(Benchmark Datasets; Data Curation; Mechanical Simulation)

Jingda Tang

Xi'an Jiaotong University, Xi'an, China

(Soft Robots; Fracture and Fatigue; Magnetic Materials)

Manuscript submission information:

Manuscript submission open date: 1 December 2023
Manuscript submission deadline: 1 April 2024

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Prof. Markus Buehler via mbuehler@MIT.EDU.

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: ML and Mechanics” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage here: https://www.sciencedirect.com/journal/extreme-mechanics-letters.

Keywords: Machine Learning; Data Science; Computation; Modelling; Scientific AI; Generative Modelling; Inverse Problems; Mechanics; Multiscale; Manufacturing