A short review on model order reduction based on proper generalized decomposition
This paper revisits a new model reduction methodology based on the use of separated
representations, the so called Proper Generalized Decomposition—PGD. Space and time …
representations, the so called Proper Generalized Decomposition—PGD. Space and time …
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality
A new data-driven computational framework is developed to assist in the design and
modeling of new material systems and structures. The proposed framework integrates three …
modeling of new material systems and structures. The proposed framework integrates three …
Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
The discovery of efficient and accurate descriptions for the macroscopic behavior of
materials with complex microstructure is an outstanding challenge in mechanics of …
materials with complex microstructure is an outstanding challenge in mechanics of …
Accelerated simulation methodologies for computational vascular flow modelling
M MacRaild, A Sarrami-Foroushani… - Journal of the …, 2024 - royalsocietypublishing.org
Vascular flow modelling can improve our understanding of vascular pathologies and aid in
developing safe and effective medical devices. Vascular flow models typically involve …
developing safe and effective medical devices. Vascular flow models typically involve …
Roadmap on multiscale materials modeling
Modeling and simulation is transforming modern materials science, becoming an important
tool for the discovery of new materials and material phenomena, for gaining insight into the …
tool for the discovery of new materials and material phenomena, for gaining insight into the …
PGD-Based Computational Vademecum for Efficient Design, Optimization and Control
In this paper we are addressing a new paradigm in the field of simulation-based engineering
sciences (SBES) to face the challenges posed by current ICT technologies. Despite the …
sciences (SBES) to face the challenges posed by current ICT technologies. Despite the …
Non-intrusive surrogate modeling for parametrized time-dependent partial differential equations using convolutional autoencoders
This paper presents a novel non-intrusive surrogate modeling scheme based on deep
learning for predictive modeling of complex systems, described by parametrized time …
learning for predictive modeling of complex systems, described by parametrized time …
[PDF][PDF] Model order reduction
This chapter presents an overview of Model Order Reduction–a new paradigm in the field of
simulationbased engineering sciences, and one that can tackle the challenges and leverage …
simulationbased engineering sciences, and one that can tackle the challenges and leverage …
Model reduction methods
This chapter presents an overview of model order reduction–a new paradigm in the field of
simulation‐based engineering sciences, and one that can tackle the challenges and …
simulation‐based engineering sciences, and one that can tackle the challenges and …
Non-intrusive low-rank separated approximation of high-dimensional stochastic models
A Doostan, AA Validi, G Iaccarino - Computer Methods in Applied …, 2013 - Elsevier
This work proposes a sampling-based (non-intrusive) approach within the context of low-
rank separated representations to tackle the issue of curse-of-dimensionality associated with …
rank separated representations to tackle the issue of curse-of-dimensionality associated with …