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Nuwan Dewapriya's picture

Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks

Abstract: Advanced machine learning methods could be useful to obtain novel insights into some challenging nanomechanical problems. In this work, we employed artificial neural networks to predict the fracture stress of defective graphene samples. First, shallow neural networks were used to predict the fracture stress, which depends on the temperature, vacancy concentration, strain rate, and loading direction.

danialfaghihi's picture

Mini-symposium on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics” at IMECE 2020 conference

Dear Colleagues,

 

As part of the IMECE 2020 (November 13-19, 2020, Portland, Oregon), we are organizing a topic on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics.” It is listed in Track 12: Mechanics of Solids, Structures, and Fluids: https://event.asme.org/IMECE/Program/Tracks-Topics.

 

Mirkhalaf's picture

PhD position on machine learning enhanced multi-scale modelling of textile composites at the University of Gothenburg

We have an open PhD position on machine learning enhanced multi-scale modelling of textile composites. The following link provides more information about the project, and the details of the application process. Please keep in mind that only applications sent through the online application system will be evaluated.

Description of the PhD project, and how to apply

 

mbessa's picture

Journal Club for February 2020: Machine Learning in Mechanics: simple resources, examples & opportunities

Machine learning (ML) in Mechanics is a fascinating and timely topic. This article follows from a kind invitation to provide some thoughts about the use of ML algorithms to solve mechanics problems by overviewing my past and current research efforts along with students and collaborators in this field. A brief introduction on ML is initially provided for the colleagues not familiar with the topic, followed by a section about the usefulness of ML in Mechanics, and finally I will reflect on the challenges and opportunities in this field.

mbessa's picture

[Deadlines updated] ICTAM2020 & WCCM2020

Dear colleagues,

Deadline to submit your abstract to ICTAM2020 and WCCM2020 is fast approacing (January 20 & 15, respectively). If you are working with machine learning, uncertainty quantification, optimization or a related topic, consider the following symposia:

vh's picture

Prediction of forming limit diagrams using machine learning

Measuring forming limit diagrams (FLDs) is a time consuming and expensive process. Machine learning (ML) methods are a promising route to predict FLD of aluminium alloys. In the present work, we developed a machine learning (ML) based tool to establish the relationships between alloy composition / thermomechanical processing route to the material's FLD.

Erik Bitzek's picture

Session on "Data driven materials science" at the DPG Spring Meeting (Dresden, Germany)

Dear colleagues, 

we would like to make you aware of the topical session 

"Data driven materials science"

which is part of the MM program during the DPG Spring Meeting 2020. The latter takes place March 15-20, 2020, in Dresden.  

If you are performing experiments or simulations in this emerging field, you are most welcome to contribute your abstract.

karelmatous's picture

A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling

Developing an accurate nonlinear reduced order model from simulation data has been an outstanding research topic for many years. For many physical systems, data collection is very expensive and the optimal data distribution is not known in advance. Thus, maximizing the information gain remains a grand challenge. In a recent paper, Bhattacharjee and Matous (2016) proposed a manifold-based nonlinear reduced order model for multiscale problems in mechanics of materials. Expanding this work here, we develop a novel sampling strategy based on the physics/pattern-guided data distribution.

mbessa's picture

PhD position @ TU Delft: Data-driven design of materials and structures

Advertising the first fully funded PhD position in my group: this position is for the more computationally/mathematically inclined. Goal: method development.

A "general audience" summary of a recent application of our work: https://www.youtube.com/watch?v=cWTWHhMAu7I

Details about the position: https://vacature.beta.tudelft.nl/vacaturesite/permalink/287309/?lang=en

vh's picture

Call for Abstracts: Numisheet 2020 mini symposium on “Challenges and Opportunities in Forming Aluminum”

The NUMISHEET conference series is the most significant international conference on the area of the numerical simulation of sheet metal forming processes. Within Numisheet 2020, we are organizing a mini symposium on “Challenges and Opportunities in Forming Aluminum”.

Jingjie Yeo's picture

Postdoctoral position in multiscale computational simulations in the J2 Lab for Engineering Living Materials

http://jingjieyeo.github.io/positions.html I am happy to announce that the website of the J2 Lab for Engineering Living Materials is now live! We're very excited to get cracking in Jan 2020 at the Sibley School of Mechanical and Aerospace Engineering in Cornell University, and we're hiring one postdoc experienced in multiscale computational simulations to kickstart our lab. Please visit our website for more details!

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: 08/01/2019

To apply, please submit your resume and a list of 3 references to JShi2@slb.com.

Introduction

Zeliang Liu's picture

[July 28] USNCCM15 Short Course on Machine Learning Data-Driven Discretization Theories, Modeling and Applications

Dear Colleagues and Friends,

In this short course, we will introduce the participants to the latest efforts on data-driven methods for mechanical and material sciences. The course will cover topics on
1. mechanistic data-driven clustering methods, direct and reduced order modeling techniques,
2. physics-informed neural networks, multi-fidelity Gaussian processes,
3. deep material networks and multiscale material failure analysis.
Some benchmarks on nano-polymer composites, polymer matrix composites, additive manufactured alloys will be demonstrated. For more details, please visit the website, http://15.usnccm.org/sc15-005.

mbessa's picture

Postdoctoral position @ TU Delft

Dear colleagues,

Richard Norte and I are looking for a postdoctoral scholar with interest in machine learning and good knowledge on finite element analyses.

This project is focused on computational mechanics in collaboration with a strong group on opto-mechanical devices.

For more information please check the following link:

https://www.academictransfer.com/nl/54445/pd-next-generation-opto-mechan...

The Machine Learning as an Expert System

1.

To cut a somewhat long story short, I think that I can ``see'' that Machine Learning (including Deep Learning) can actually be regarded as a rules-based expert system, albeit of a special kind.

I am sure that people must have written articles expressing this view. However, simple googling didn’t get me to any useful material.

I would deeply appreciate it if someone could please point out references in this direction. Thanks in advance.

2.

Markus J. Buehler's picture

Postdoc positions at MIT available

We have one or more postdoc positions available, to be filled immediately, at MIT’s Laboratory for Atomistic and Molecular Mechanics, under the direction of Professor Markus Buehler. We are looking for postdocs in two broad areas, as described below. 

Position #1: Materials science modeling 

PhD positions in Controls are available at George Mason University

Applications are invited for PhD positions at the Algorithms in Medicine and Neuro-Technology Lab (AIMAN Lab) in the Department of Mechanical Engineering at George Mason University, Fairfax, VA. "Research" The AIMAN Lab pursues fundamental breakthroughs in biomedical cyber-physical systems.

PhD positions in Controls (ME Department) are available at George Mason University

Applications are invited for PhD positions at the Algorithms in Medicine and Neuro-Technology Lab (AIMAN Lab) in the Department of Mechanical Engineering at George Mason University, Fairfax, VA. "Research" The AIMAN Lab pursues fundamental breakthroughs in biomedical cyber-physical systems.

Urgent: 2 fully-funded PhD theses at Synopsis (Exeter) / Cardiff University / University of Luxembourg in machine learning/biomedical simulations/3D medical image processing

Synopsys NE Ltd (https://www.synopsys.com/simpleware.html), Cardiff University and University of Luxembourg invites applications for 2 Early Stage Researcher position (Doctoral Candidate) as part of the Rapid Biomechanics and Simulation for Personalized Clinical Design (RAINBOW) MCSA European Training Network. RAINBOW is funded under the European Union’s Horizon 2020 research and innovation program.
 

PhD positions in Control are available at George Mason University, ME Department

Applications are invited for PhD positions at the Algorithms in Medicine and Neuro-Technology Lab (AIMAN Lab) in the Department of Mechanical Engineering at George Mason University, Fairfax, VA. "Research" The AIMAN Lab pursues fundamental breakthroughs in biomedical cyber-physical systems.

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