Abstract Submissions

Call for Abstracts

Submit your abstract by clicking on the Track title below.

Abstract submissions closed on May 15, 2021.

Abstract Preparation Guidelines

A complete electronic submission of your abstract must include the following:

  • Each abstract can only be submitted to and be affiliated with a particular mini-symposium.

  • The title should be less than 20 words. Avoid acronyms, and use sentence case capitalization (e.g., This is my abstract title without acronyms).

  • Abstracts should only contain text and is limited to 400 words.

  • The submitting author will be automatically designated as the corresponding author.

  • Identify and include the presenting author full name, title, and email.

  • Co-author(s) full name and email.

  • At the time of submission, select whether you preferred mode of conference participation: "On site", "Remote", or "Undecided". This is used for planning purposes and can be changed when registering for the conference.

To submit your abstract, navigate to the appropriate Track below and click on the "Submit" link. The link will take you to the Morressier abstract submission portal. Account registration with Morressier will be required.

Abstracts will be published in the form of an online and searchable proceedings.

Tracks and Mini-Symposia Organization and Abstract Submission Links

    • 1-1 Nonlocal Operators and Machine Learning in Multiscale Modeling

    • 1-2 Machine learning in Polymer Science and Chemistry

    • 1-3 Studying material response using Machine Learning

    • 1-4 Identifying Constitutive Behavior and Dynamics via Physics-informed Machine Learning.

    • 1-5 Data-driven Approaches in Computational Solid Mechanics

    • 1-6 Machine Learning and Multiscale Modeling for Complex Materials System

    • 1-7 Machine Learning for Uncertainty Quantification in Engineering Systems

    • 1-8 Geometric Learning for Mechanistic Modeling and Material Designs

    • 1-9 Machine Learning and Generative Design for Additive Manufacturing

    • 2-1 Advanced Computational Technologies Enabling Digital Twins

    • 2-2 Statistical Finite Element Methods for Synthesizing Models and Observations

    • 2-3 Advances in Data-driven Methods and Digital Twins for Advanced Manufacturing

    • 2-4 Credible Image-Based Simulations for Digital Twins

    • 2-5 Data Driven Approaches for Circuit Design and Analysis

    • 2-6 Merging Simulation and Machine Learning for Industrial Applications

    • 2-7 Physics-Based Data-Driven Modeling and Machine Learning for Intelligent and Green Transportation

    • 2-8 Integration of Models and Data and Artificial Intelligence for Energy and Power Systems

    • 2-9 RISE of the Machines: Robust Interpretable Scalable Efficient Decision Support

    • 2-10 Deep and Machine Learning Methodology in the Context of Application to Computational Mechanics

    • 3-1 Data-Driven Modeling and Simulation for Computational Biomedicine

    • 3-2 Numerical Twins for Biological Systems

    • 4-1 Machine-learning-based Models for Forward and Inverse Problems in Computational Science and Engineering

    • 4-2 Hybrid Data-Driven and Physics-Based Model Reduction in Mechanical Systems

    • 4-3 Machine Learning and Data Diversity

    • 4-4 Extreme Events in Complex Systems: Statistical Prediction and Quantification and Mitigation

    • 4-5 Data-driven Reduced-order Methods for System Control

    • 4-6 Machine Learning Closures for Multiphase Flows

    • 4-7 Physics-informed Data-based Model Reduction

    • 4-8 Model Reduction and Machine Learning for Fluids and Fluid-structure Interactions

    • 4-9 Advances in Reduced Order Modeling of Solids and Fluids and Porous Media

    • 4-10 Model Reduction of Dynamical Systems with Deep Learning

    • 5-1 Advances in Machine Learning and Model Reduction for Inversion in Geophysical and Geological Applications

    • 5-2 Advances in Machine Learning Algorithms in Geosciences and Reservoir Engineering Applications

    • 5-3 Machine Learning Enhanced Multi-scale Modeling and Imaging Technologies for Geosystems Applications

    • 5-4 Data-driven Methods in Geophysics

    • 5-5 Machine Learning for Petroleum Engineering Applications

    • 5-6 Multiscale Machine Learning for Geotechnical and Geophysics and Geomechanics Applications: from Grain- to Field-scales

    • 6-1 Machine Learning and Data Centric Engineering in Civil Engineering Applications

    • 6-2 Human-Society-Machine-Infrastructure Interfaces

    • 6-3 Civil Infrastructure Digital Twins for Life Cycle Asset Management

    • 6-4 Physics-Informed Machine Learning for Smart City

    • 6-5 Recent Advanced in Hybrid Numerical and Experimental Simulation

    • 6-6 Technologies for the Rapid Assessment and Monitoring of the Integrity of Structures Exposed to Earthquakes and other Hazards

    • 7-1 Progress of Machine Learning and Digital Twins in Computational Mechanics for Industrial Applications

Track 8: Panel for Education, Outreach, and Funding Opportunities

Organizing Universities

UC San Diego, Northwestern University, Stanford University, Brown University, Columbia University, and Arts et MĂ©tiers Institute of Technology of France

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