machine learning

Hanxun Jin (a,b), Horacio D. Espinosa (b)
a Division of Engineering and Applied Science, California Institute of Technology
b Department of Mechanical Engineering, Northwestern University

In recent years, Machine Learning (ML) has become increasingly prominent in Solid Mechanics. Its diverse applications include extracting unknown material parameters, developing surrogate models for constitutive modeling, advancing multiscale modeling, and designing architected materials. In this Journal Club, we will focus our discussion on the recent advances and challenges of ML when experimental data is involved. With broad community interest, as reflected by the increasing number of publications in this field, we have recently published a review article in Applied Mechanics Reviews titled “Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review”. Moreover, a recent insightful paper from Prof. Sam Daly’s group also discussed some perspectives in this field. In this Journal Club, we would like to introduce and share insights into this exciting field.

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.

We are looking for a postdoctoral researcher to work on the project MECA-ICTUS, a 3-year project funded under the Generación de Conocimiento 2022 call of Agencia Estatal de Investigación. In MECA-ICTUS we will pursue the development of computational mechanics and machine learning tools for predicting the success of endovascular thrombectomy, an urgent intervention for the removal of thrombi in Acute Ischemic Stroke Patients.

Multiple postdoc and one PhD positions are open at Tsinghua University. The research will take place at Tsinghua University’s Shenzhen International Graduate School (SIGS), located in Shenzhen, China, and is partly sponsored by NSFC and Tsinghua SIGS’s scientific research startup funds.

We invite applications for post-doctoral and Ph.D. positions in machine learning, multi-physics simulation and uncertainty qualification for additive manufacturing processes. 

For additional information, please contact Prof. Xiaoping Qian (qian [at] engr.wisc.edu (qian[at]engr[dot]wisc[dot]edu)). To apply, email your application files (CV, 1-page summary of research accomplishments and research interests, and 3 references).

The Uncertainty Quantification (UQ) Lab at the University of Houston (UH), led by Dr. Ruda Zhang, invites applications for:

• two (2) PhD student positions in areas of data-driven engineering and uncertainty quantification, and
• one (1) postdoctoral researcher in areas of surrogate modeling and data-driven dynamics.

For job details and updates, see lab webpage: https://uq.uh.edu/positions

Joining: https://wwwfr.uni.lu/formations/fstm/doctoral_programme_in_computationa…

PhD candidate on Machine Learning applied to Critical Zone sciences 

Ph.D. positions are available at the Multiscale Material Modeling Lab at the University of North Carolina at Charlotte. The Ph.D. students will develop and use tools such as machine learning, finite elements, and molecular dynamics to study the mechanical properties of materials. Candidates with a strong background and interest in solid mechanics, programming, and computational solid mechanics are encouraged to submit their CV to atabarra [at] uncc.edu (atabarra[at]uncc[dot]edu).