reduced order modeling

1 Early Stage Researcher (ESR) PhD position in biomechanics offered within the framework of the project MeDiTATe (The medical digital twin for aneurysm prevention and treatment) funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions (MSCA) Innovative Training Networks (ITN) with Grant Agreement No. 859836. MeDiTATe is a European Industrial Doctorate (EID).

Modeling Uncertainties in Molecular Dynamics Simulations Using A Stochastic Reduced-Order Basis

Submitted by Haoran Wang on Mon, 06/17/2019 - 02:56

research

uncertainty quantification

molecular dynamics

graphene

reduced order modeling

We've recently published our new study about Uncertainty Quantification in Molecular Dynamics (MD) Simulations. Due to the selection of functional forms of interatomic potentials or the numerical approximation, MD simulations may predict different material behavior from experiments or other high-fidelity results. In this study, we used Stochastic Reduced Order Modeling (SROM) to achieve

(1) mechanical behavior of graphene predicted by MD simulations in good agreement with the continuum model which has been calibrated by experiments;

[June 5 | USNC/TAM2018] Short-course on data-driven modeling in mechanics and materials science

Submitted by mbessa on Sun, 03/04/2018 - 23:53

artificial intelligence

machine learning

Optimization

reduced order modeling

Dear colleagues in academia and industry,

We are organizing the first short course on data-driven computational mechanics and materials science at the 18th US National Congress on Theoretical and Applied Mechanics (USNC/TAM) in Chicago on June 5th, 2018. See description of short-course SC007 in the following website: http://sites.northwestern.edu/usnctam2018/short-courses/

FILLED: Ph.D. position available in France/Belgium: Optimization methods supported by multi-fidelity full-field meta-models and enrichment strategies dedicated to turbine engine design

Submitted by breit on Fri, 11/17/2017 - 14:55

job

design optimization

reduced order modeling

turbomachinery

CIFRE program

Thank his post has been filled and is no longer available

MS1801 @ WCCM2018 -> Data-driven Methods and Applications: from Physics-informed Learning Machines to Optimization Under Uncertainty

Submitted by mbessa on Sun, 10/15/2017 - 22:23

conference

machine learning

reduced order modeling

Optimization

uncertainty quantification

Dear colleagues,

We encourage you to submit your abstracts to minisymposium 1801 of the 13th World Congress on Computational Mechanics (New York City, from July 22 to 27 of 2018). This minisymposium focuses on:

1. recently developed methods for data-driven approaches;

2. data-driven applications to fluids, structures and materials involving (but not limited to) machine learning, uncertainty quantification and/or optimization.

A nonlinear manifold-based reduced order model

Submitted by karelmatous on Sun, 03/13/2016 - 18:17

research

computational homogenization

nonlinear manifold

reduced order modeling

machine learning

parallel computing

A new perspective on model reduction for nonlinear multi-scale analysis of heterogeneous materials. In this work, we seek meaningful low-dimensional structures hidden in high-dimensional multi-scale data.