Sensitivity analysis of a multi‐physics model for the vascular microenvironment
The vascular microenvironment is the scale at which microvascular transport, interstitial
tissue properties and cell metabolism interact. The vascular microenvironment has been …
tissue properties and cell metabolism interact. The vascular microenvironment has been …
[HTML][HTML] Nonlinear model order reduction for problems with microstructure using mesh informed neural networks
Many applications in computational physics involve approximating problems with
microstructure, characterized by multiple spatial scales in their data. However, these …
microstructure, characterized by multiple spatial scales in their data. However, these …
Modeling hypoxia-induced radiation resistance and the impact of radiation sources
Hypoxia contributes significantly to resistance in radiotherapy. Our research rigorously
examines the influence of microvascular morphology on radiotherapy outcome, specifically …
examines the influence of microvascular morphology on radiotherapy outcome, specifically …
Geometrically reduced modelling of pulsatile flow in perivascular networks
Flow of cerebrospinal fluid in perivascular spaces is a key mechanism underlying brain
transport and clearance. In this paper, we present a mathematical and numerical formalism …
transport and clearance. In this paper, we present a mathematical and numerical formalism …
Mesh-informed neural networks for operator learning in finite element spaces
Thanks to their universal approximation properties and new efficient training strategies,
Deep Neural Networks are becoming a valuable tool for the approximation of mathematical …
Deep Neural Networks are becoming a valuable tool for the approximation of mathematical …
Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”?
CC Reyes-Aldasoro - Cancers, 2023 - mdpi.com
Simple Summary The word “model” can be used with different meanings and different
contexts, like a model student, clay models or a model railway. In some cases, the context …
contexts, like a model student, clay models or a model railway. In some cases, the context …
Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy
Forward uncertainty quantification (UQ) for partial differential equations is a many-query task
that requires a significant number of model evaluations. The objective of this work is to …
that requires a significant number of model evaluations. The objective of this work is to …
Modeling oxygen transport in the brain: An efficient coarse-grid approach to capture perivascular gradients in the parenchyma
Recent progresses in intravital imaging have enabled highly-resolved measurements of
periarteriolar oxygen gradients (POGs) within the brain parenchyma. POGs are increasingly …
periarteriolar oxygen gradients (POGs) within the brain parenchyma. POGs are increasingly …
Reduced Lagrange multiplier approach for non-matching coupling of mixed-dimensional domains
Many physical problems involving heterogeneous spatial scales, such as the flow through
fractured porous media, the study of fiber-reinforced materials, or the modeling of the small …
fractured porous media, the study of fiber-reinforced materials, or the modeling of the small …
[PDF][PDF] Learning operators with mesh-informed neural networks
Thanks to their universal approximation properties and new efficient training strategies,
Deep Neural Networks are becoming a valuable tool for the approximation of mathematical …
Deep Neural Networks are becoming a valuable tool for the approximation of mathematical …