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MS Invitation "Multiscale Machine Learning for Geotechnical and Geophysics and Geomechanics Applications: from Grain- to Field-scales" @IACM conf. MMLDT-CSET 2021

WaiChing Sun's picture

Dear Colleagues,

We would like to invite you to submit an abstract to the symposium on Multiscale Machine Learning for Geotechnical and Geophysics and Geomechanics Applications: from Grain- to Field-scales at the MMLDT-CSET 2021 conference, September 26-29:

Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET 2021)
An IACM Conference September 26-29, 2021 - Hyatt Regency Mission Bay, San Diego, USA
(website: https://mmldt.eng.ucsd.edu/)
 
*The conference will be organized in a hybrid format consisting of virtual and on-site sessions

Abstract Submission is now open until March 1st. The description of the symposium is as follows:

Title:
Multiscale Machine Learning for Geotechnical, Geophysics and Geomechanics Applications: from Grain- to Field-scales

Abstract:
This mini-symposium focuses on data-driven machine learning approaches that enable ideas, methodologies and algorithms, and techniques that enable predictions for geomechanics, geohydrology, rock physics and geophysical problems. Special emphasis is placed on the applications that may link predictions or models across multiple length and spatial scales. Topics of interest may include but are not limited to applications of convolutional neural networks to predict seismic activities, big data applications for landslide predictions, constitutive modeling of geomaterials, physical-informed neural networks for poromechanics and multi-phase flow simulations, machine learning/data-driven accelerated multiscale homogenization, data-driven/model-free methods, as well as uncertainty quantification and digital twin approaches for reservoir management and waste disposal.

Sincerely,
The symposium Co-organizers:
WaiChing Sun, Columbia University
Kane Bennett, Los Alamos National Laboratory
Hari Viswanathan, Los Alamos National Laboratory
Paul Johnson, Los Alamos National Laboratory

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