Computer Methods for Direct and Inverse Modelling and Simulation

Dear Colleagues,

Computer methods for the simulation and inverse analysis of natural or human-induced phenomena have reached vital importance both in questions of practical life and in scientific and technological research. The refinement of the procedures has caused considerable specialization and the growth of a great number of different approaches. In the recent development of techniques, the integration of classic methods is increasingly widespread, especially with techniques borrowed from the CAD and gaming fields. In addition, techniques derived from neural networks and machine learning are becoming popular. Computer methods are applied in any branch of knowledge. However, scientists from different research areas often share the same background and basic numerical techniques.

This Special Issue is focused on collecting works from multiple fields of science and engineering that propose different and innovative visions of the main techniques and advances in the application of computational methods for direct and inverse analyses.

A non-exhaustive list of the topics to be discussed is the following:

• Numerical and Discrete Mathematics
• Fracture, Damage, and Failure Mechanics
• Advanced Discretization Techniques (Virtual, Finite, Boundary Element, Multipole, Meshless)
• Multiscale and Multiphysics Systems
• Biomechanics and Mechanobiology
• Materials Discovery; Composites, Multifunctional, and Structural Materials; MetaMaterials
• Computational Fluid Dynamics and Transport Phenomena
• Numerical Methods and Algorithms in Science and Engineering
• Computational Electromagnetics
• Numerical Techniques in Direct and Inverse Scattering
• EM Modelling and Simulation Tools
• Uncertainty, Reliability, and Error Estimation
• Structural Mechanics, Dynamics, and Engineering
• Nano- and Micromechanics of Materials
• Inverse Problems, Optimization, and Design
• Fluid-structure Interaction, Contact, and Interfaces
• Geomechanics and Natural Materials
• Data-Driven Analysis and Machine Learning
• Imaging and Visualization

Prof. Dr. Vincenzo Minutolo
Prof. Dr. Adriana Brancaccio
Prof. Dr. Andrea Vacca
Guest Editors

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