The development of computational methods for the solution of scientific and engineering problems governed by the laws of mechanics was one of the great scientific and engineering achievements of the second half of the 20th century, with a profound impact on science and technology. This is accomplished through advanced mathematical modeling and numerical solutions reflecting a combination of concepts, methods and principles that are often interdisciplinary in nature and span several areas of mechanics, mathematics, computer science and other scientific disciplines as well.

Computer Methods in Applied Mechanics and Engineering was founded over five decades ago, providing a platform for the publication of papers in this important field of computational science and engineering. The range of appropriate contributions is very wide. It covers any type of computational method for the simulation of complex physical problems leading to the analysis and design of engineering products and systems. This includes theoretical development and rational applications of mathematical models, variational formulations, and numerical algorithms related to finite element, boundary element, finite difference, finite volume, isogeometric and meshless discretization methods in the following fields of of simulation-based engineering science:

• Solid and structural mechanics
• Fluid mechanics
• Mechanics of materials
• Heat transfer
• Dynamics
• Geomechanics
• Acoustics
• Biomechanics
• Nanomechanics
• Molecular dynamics
• Quantum mechanics
• Electromagnetics

and also includes virtual design, multiscale phenomena, from nanoscale to macroscale, multiphysics problems, parallel computing, optimization, machine learning, probabilistic and stochastic approaches.

CMAME publishes original papers at the forefront of modern research describing significant developments of computational methods in solving problems of applied mechanics and engineering.

Special Issue on Generative Artificial Intelligence for Predictive Simulations and Decision-Making in Science and Engineering (By invitation only)
Submission Date: 2024-08-01

The advent of generative artificial intelligence (AI) marks a paradigm shift in AI that also fosters unprecedented advancements in predictive simulations and decision-making in the sciences and engineering. This special issue focuses on generative AI methodologies that meet the unique demands of science and engineering applications, such as limited data availability, complex physics that are governed by nonlinear, non-stationary, and multi-scale phenomena, dynamic environments with rapidly changing conditions, and domain knowledge provided in the form of physical laws and constraints. Furthermore, generative AI techniques for high-consequence decision-making in science and engineering require rigorous validation and verification, robustness, interpretability, and uncertainty quantification for maintaining the integrity of the overall process. This special issue aims to capture the current state and articulate the future directions of this rapidly evolving field, fostering a dialogue among researchers in mathematics, computational sciences, and engineering.

Guest editors:

Prof. Youssef Marzouk
Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America

Assoc. Prof. Benjamin Peherstorfer
New York University, New York, New York, United States of America

Manuscript submission information:

    Guest Editor Invitation Only
    Open for Submission: from 22-Mar-2024 to 01-Aug-2024
    Submission Site: Editorial Manager®
    Article Type Name: "VSI: GenAI4Science" - please select this item when you submit invited-manuscripts online

All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue.

Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Computer Methods in Applied Mechanics and Engineering

For more information about our Journal, please visit our ScienceDirect Page: Computer Methods in Applied Mechanics and Engineering | Journal | ScienceDirect.com by Elsevier

Keywords:

    generative artificial intelligence; 
    scientific machine learning; 
    flow- and diffusion-based modeling; 
    reduced and latent modeling; 
    probabilistic forecasting; 
    Monte Carlo sampling; 
    active learning

Special Issue on Advanced Machine Learning for Uncertainty Quantification (By invitation only)
Submission Date: 2024-08-30

In the field of Computational Science and Engineering, a disruptive force has emerged over recent years, reshaping the boundaries of what we can achieve: Machine Learning. With its ability to extract patterns and insights from vast amounts of data, machine learning has pushed computational science and engineering to new heights, unlocking innovative solutions to intricate problems and paving the way for a new era in predictive science.

Given the increasing complexity of engineered systems, it has become imperative to create cost-effective predictive models for various challenging applications such as reliability analysis, uncertainty quantification, and system design optimization. Machine Learning has demonstrated its potential in creating data-driven surrogate models that can replace the computationally expensive physics-based high-fidelity models. These surrogate models not only help with uncertainty quantification but also facilitate the continuous improvement of system design in the presence of uncertainties.

Despite these advancements, there are ongoing difficulties in enhancing the accuracy of predictive models that simulate stochastic engineering systems. Focusing on the intersection of machine learning and uncertainty modeling, the issue aims to bring together contributions that push the boundaries of current practices. The goal is to provide a comprehensive overview of the state-of-the-art in advanced machine learning techniques dedicated to quantifying uncertainties, fostering a deeper understanding of uncertainty in complex systems, and promoting the development of more robust and reliable models.

Topic Areas

    Surrogate models for forward and inverse UQ
    Multi-fidelity and multi-level UQ using machine learning
    Statistical surrogate models
    Probabilistic manifold learning
    Data-driven material science
    Deep Bayesian models for Uncertainty Propagation
    Sparse Gaussian Processes for Efficient UQ
    Time Series Uncertainty Modeling
    Machine learning-enhanced iterative solvers for large-scale stochastic problems
    Linear and nonlinear dimensionality reduction techniques
    Uncertainty quantification of machine learning models
    Applications of advanced machine learning methods for uncertainty quantification in Computational Fluid Dynamics, Structural Analysis, Multiscale and Multiphysics problems

Guest editors:

Prof. Vissarion Papadopoulos
Affiliation: National Technical University of Athens (NTUA), Athens, Greece

Prof. Eleni Chatzi
Affiliation: ETH Zürich, Zurich, Switzerland

Prof. Roger Ghanem
Affiliation: University of Southern California, Los Angeles, United States

Prof. Christian Soize
Affiliation: Université Gustave Eiffel, Champs-sur-Marne, France

Manuscript submission information:

    Guest Editor Invitation Only
    Open for Submission: from 11-Mar-2024 to 30-Aug-2024
    Submission Site: Editorial Manager®
    Article Type Name: "VSI: ML for UQ" - please select this item when you submit manuscripts online