[HTML][HTML] Inverse Physics-Informed Neural Networks for transport models in porous materials
Abstract Physics-Informed Neural Networks (PINN) are a machine learning tool that can be
used to solve direct and inverse problems related to models described by Partial Differential …
used to solve direct and inverse problems related to models described by Partial Differential …
A practical existence theorem for reduced order models based on convolutional autoencoders
NR Franco, S Brugiapaglia - arXiv preprint arXiv:2402.00435, 2024 - arxiv.org
In recent years, deep learning has gained increasing popularity in the fields of Partial
Differential Equations (PDEs) and Reduced Order Modeling (ROM), providing domain …
Differential Equations (PDEs) and Reduced Order Modeling (ROM), providing domain …
[HTML][HTML] Proper orthogonal decomposition method of constructing a reduced-order model for solving partial differential equations with parametrized initial values
This paper proposes a novel proper orthogonal decomposition method for constructing a
reduced-order model. This model effectively computes solutions for various initial conditions …
reduced-order model. This model effectively computes solutions for various initial conditions …
Physics informed neural networks for an inverse problem in peridynamic models
Deep learning is a powerful tool for solving data driven differential problems and has come
out to have successful applications in solving direct and inverse problems described by …
out to have successful applications in solving direct and inverse problems described by …
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 …
Physics informed neural networks for learning the horizon size in bond-based peridynamic models
FV Difonzo, L Lopez, SF Pellegrino - Computer Methods in Applied …, 2025 - Elsevier
This paper broaches the peridynamic inverse problem of determining the horizon size of the
kernel function in a one-dimensional model of a linear microelastic material. We explore …
kernel function in a one-dimensional model of a linear microelastic material. We explore …
Unveiling the biological side of PET-derived biomarkers: a simulation-based approach applied to PDAC assessment
L Cavinato, J Hong, M Wartenberg, S Reinhard… - European Journal of …, 2024 - Springer
Purpose Radiomics has revolutionized clinical research by enabling objective
measurements of imaging-derived biomarkers. However, the true potential of radiomics …
measurements of imaging-derived biomarkers. However, the true potential of radiomics …
An efficient reduced order model for nonlinear transient porous media flow with time-varying injection rates
Abstract An intrusive Reduced Order Model (ROM) is developed for nonlinear porous media
flow problems with transient and time-discontinuous fluid injection rates. The proposed ROM …
flow problems with transient and time-discontinuous fluid injection rates. The proposed ROM …
Deep orthogonal decomposition: a continuously adaptive data-driven approach to model order reduction
We develop a novel deep learning technique, termed Deep Orthogonal Decomposition
(DOD), for dimensionality reduction and reduced order modeling of parameter dependent …
(DOD), for dimensionality reduction and reduced order modeling of parameter dependent …
An efficient reduced order model for nonlinear transient porous media flow with time-varying injection rates
Abstract An intrusive Reduced Order Model (ROM) is developed for nonlinear porous media
flow problems with transient and time-discontinuous fluid injection rates. The proposed ROM …
flow problems with transient and time-discontinuous fluid injection rates. The proposed ROM …