Results 81 to 90 of about 18,341 (302)

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings

We present a novel approach to hard-constrain Neumann boundary conditions in physics-informed neural networks (PINNs) using Fourier feature embeddings. Neumann boundary conditions are used to described critical processes in various application, yet they ...
Medvedev, Vlad, Straub, Christopher, Brendel, Philipp, Roßkopf, Andreas   +3 more
core   +1 more source

A Numerical–Experimental Approach for Multi‐Matrix Fiber‐Reinforced Plastics Characterization Using Finite Element Model Updating

Advanced Engineering Materials, EarlyView.
A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora, Tales V. Lisbôa, Axel Spickenheuer, Rogério J. Marczak, Jakson M. Vassoler   +4 more
wiley   +1 more source

Inverse Physics-Informed Neural Networks for transport models in porous materials

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 Equations by including in the cost function to minimise during training the ...
Icardi, Matteo, Berardi, Marco, Difonzo, Fabio V.   +2 more
core   +1 more source

Physics Informed Neural Networks for Engineering Systems

, 2020
This thesis explores the application of deep learning techniques to problems in fluid mechanics, with particular focus on physics informed neural networks.
Sukirt, None
core  

Towards Stable Training of Complex-Valued Physics-Informed Neural Networks: A Holomorphic Initialization Approach

Mathematics
This work introduces a new initialization scheme for complex-valued layers in physics-informed neural networks that use holomorphic activation functions.
Andrei-Ionuț Mohuț, Călin-Adrian Popa
doaj   +1 more source

Robust Variational Physics-Informed Neural Networks

Computer Methods in Applied Mechanics and Engineering
We introduce a Robust version of the Variational Physics-Informed Neural Networks method (RVPINNs). As in VPINNs, we define the quadratic loss functional in terms of a Petrov-Galerkin-type variational formulation of the PDE problem: the trial space is a (Deep) Neural Network (DNN) manifold, while the test space is a finite-dimensional vector space ...
Rojas, Sergio, Maczuga, Paweł, Muñoz-Matute, Judit, Pardo, David, Paszyński, Maciej   +4 more
openaire   +3 more sources

Additive Manufacturing of Continuous Fibre Reinforced Composites: Process, Characterisation, Modelling, and Sustainability

Advanced Engineering Materials, EarlyView.
Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley   +1 more source

Structural analysis of 2D frame structures using Physics Informed Neural Networks: Calculation files and result data

Within the present study, a framework for Physics Informed Neural Networks (PINN) is formulated for the analysis of frame structures in two dimensions.
Most, Thomas, Könke, Carsten, Deckert, Felix, Lippold, Lukas   +3 more
core   +1 more source

Physics Informed Neural Networks for Parametrized Partial Differential Equations

, 2023
Physics Informed Neural Networks for Parametrized Partial Differential ...
Prieto Ruiz, Victor Scott
core