The numerical simulation of sheet metal forming processes depends on the accuracy of the
constitutive model used to represent the mechanical behaviour of the materials. The …

Abstract Topological Data Analysis (TDA) is a discipline that applies algebraic topology
techniques to analyze complex, multi-dimensional data. Although it is a relatively new field …

In the present work, a machine learning based constitutive model for electro-mechanically
coupled material behavior at finite deformations is proposed. Using different sets of …

Science faces challenges in developing much-needed precision mental health treatments to
accurately identify and diagnose mental health problems and the optimal treatment for each …

The extraordinary success of Machine Learning (ML) in many complex heuristic fields has
promoted its introduction in more analytical engineering fields, improving or substituting …

To ensure correct filling in the resin transfer molding (RTM) process, adequate numerical
models have to be developed in order to correctly capture its physics, so that this model can …

Despite the existence of computationally efficient tools, the effort for parametric
investigations is currently high in industry. In this paper, within the context of Li-Ion batteries …

This paper presents an advanced meta-modeling framework that efficiently combines
Machine Learning and Model Order Reduction (MOR) techniques for real-time virtual testing …

Topological Data Analysis (TDA) is a mathematical method using techniques from topology
for the analysis of complex, multi-dimensional data that has been widely and successfully …

Soil-filled wire and geotextile gabions stand as vital bulwarks in military bases, harnessing
soil's innate capacity to absorb shock and safeguard both personnel and critical assets from …