susanta's blog

Join Zoom Meeting: (https://us06web.zoom.us/j/84541274013?pwd=ZDhuM09BaUlVVlBuREhEV0sweGFlZz09: Meeting ID: 845 4127 4013/Passcode: 799065)

Date/Time: February 28; 2-3pm CST

Speaker: Gideon Simpson, Drexel University

Discussant: Petr Plechac, University of Delaware

Title: Learning dynamics with random Fourier features

USACM Nanotechnology and Lower Scale Phenomena TTA,  webinar is on Jan 31, at 2-3 pm central time.

Speaker: Prof. Surya R. Kalidindi from Georgia Institute of Technology

We have Prof. Raymundo Arróyave of Texas A&M University as the discussant.

We are hoping the format will promote a lively interactive discussion and engage members of our community. We look forward to seeing you there.  Please forward this to anyone who may be interested.

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Please forward this message to anyone who you feel might be interested in this position. 

We are looking for a self-motivated Ph.D. student to study Machine Learning enabled computational models for applications in Material Modeling and Computational Mechanics, starting August 2024. This project will draw ideas from various subjects including Bayesian Machine Learning, Scientific Machine Learning, Projection-based model reduction, Computational Mechanics, and Medical Imaging. This is a collaborative project between Michigan Tech and University of California, Los Angeles.

Our upcoming webinar is on September 27 at 2pm central.

Date/Time: September 27; 2-3pm CDT

Join via Zoom: https://us06web.zoom.us/j/84541274013?pwd=ZDhuM09BaUlVVlBuREhEV0sweGFlZz09

(Meeting ID: 845 4127 4013/Passcode: 799065)

Speaker: Kaushik Bhattacharya, California Institute of Technology

Discussant: Miguel Bessa, Brown University

Upcoming USACM Nanotechnology webinar on Wednesday, Aug 30, 2:00 pm - 3:00 pm CDT.

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https://us06web.zoom.us/j/84541274013?pwd=ZDhuM09BaUlVVlBuREhEV0sweGFlZz09

Meeting ID: 845 4127 4013                Passcode: 799065

The Kullback-Leibler (KL) divergence is widely used in state-of-the-art Bayesian Neural Networks (BNNs) to ap- proximate the posterior distribution of weights. However, the KL divergence is unbounded and asymmetric, which may lead to instabilities during optimization or may yield poor generalizations. To overcome these limitations, we examine the Jensen-Shannon (JS) divergence that is bounded, symmetric, and more general. Towards this, we propose two novel loss functions for BNNs.

Funded Ph.D. position in the Department of Mechanical Engineering- Engineering Mechanics at the Michigan Technological University. Starting June/September 2023. 

Kindly forward this message to anyone who you feel might be interested in this position. 

We are looking for a self-motivated Ph.D. student to study Machine Learning enabled computational models for applications in Material Modeling and Computational Mechanics. 

Highlights

• A finite-deformation crystal-elastic membrane model for TMD monolayers is presented.

• Strains of the middle surface and two normal-stretches describe the deformation.

• The continuum hyperelastic strain energy is obtained from an interatomic potential.

• The present model matches well with the purely atomistic simulations.

Abstract

Hi,

Our department of Mechanical Engineering-Engineering Mechanics (MEEM) at Michigan Tech is currently searching for outstanding new colleagues in space technology, aerospace technology, NVH, acoustics/noise control, composites, and electric vehicles. Still, we are also open to other fields. We are currently looking to fill tenure-track positions at all levels.  Finding and encouraging talented senior graduate students, postdocs, and colleagues to apply for opportunities is challenging. 

Dear Colleagues:

We are co-organizing a mini-symposium titled “Physics-Based and Data-Driven Multiscale Modeling of Nano-Materials”, (#318) for the upcoming USNC/TAM 2022, to be held in Austin, Texas, June 19-24, 2022.  

Funded Ph.D. position in the Department of Mechanical Engineering- Engineering Mechanics at the Michigan Technological University. Starting June or September 2019. 

We are looking for a self-motivated Ph.D. student to study Machine Learning enabled computational models for applications in Biomedical Imaging and Material Modeling. 

We are looking for a self-motivated PhD student to study Machine Learning enabled computational models for applications in Biomedical Imaging and Multi-Scale Material Modeling. 

This project will draw ideas from various subjects including Computational Mechanics, Machine Learning and Materials Physics. This is a collaborative project between MTU and University of Wisconsin, Madison. 

Dear Colleagues, 

Dr. Dbakar Datta and I are co-organizing a session titled “Mechanics and Physics of 2D Crystalline Materials” (Track #1203) for the upcoming WCCM 2018 conference in New York City.

We would like to cordially invite you to submit an abstract to our session. The topics of this symposium include (but not limited to) the following:

We are looking for a self-motivated PhD student to study Multi-Scale Material Modeling for different material systems such as Graphene and Polymeric Materials. This position is supported by the MEEM Department at MTU. This project will draw ideas from various subjects including Computational Mechanics and Materials Physics.

The candidate would have access to the advanced computing facilities, collaborations with researchers in other universities, and experimental collaborations. 

The mechanical behavior of mono- and multi- layered graphene and carbon nanotube (CNT) systems has attracted great attention over the last decade because of their importance in nano-science and nanotechnology.