High-Fidelity Reconstruction of 3D Temperature Fields Using Attention-Augmented CNN Autoencoders with Optimized Latent Space
Understanding and accurately predicting complex three-dimensional (3D) temperature
distributions are critical in diverse domains, including climate science and industrial process …
distributions are critical in diverse domains, including climate science and industrial process …
Machine Learning for the Design and the Simulation of Radiofrequency Magnetic Resonance Coils: Literature Review, Challenges, and Perspectives
Radiofrequency (RF) coils for magnetic resonance imaging (MRI) applications serve to
generate RF fields to excite the nuclei in the sample (transmit coil) and to pick up the RF …
generate RF fields to excite the nuclei in the sample (transmit coil) and to pick up the RF …
GenAI-Enhanced Federated Multi-Agent DRL for Digital Twin-Assisted IoV Networks
Achieving real-time decision-making and efficient resource management in dynamic, large-
scale Internet-of-Vehicles (IoV) networks is a significant challenge due to their inherent …
scale Internet-of-Vehicles (IoV) networks is a significant challenge due to their inherent …
A Novel Hybrid Boundary Element–Physics Informed Neural Network Method for Numerical Solutions in Electromagnetics
In this contribution the authors propose a hybrid Boundary Element Method–Physics
Informed Neural Networks (BEM–PINN) approach, to be used for the resolution of partial …
Informed Neural Networks (BEM–PINN) approach, to be used for the resolution of partial …
Machine Learning Approaches for Inverse Problems and Optimal Design in Electromagnetism
The spread of high-performance personal computers, frequently equipped with powerful
Graphic Processing Units (GPUs), has raised interest in a set of techniques that are able to …
Graphic Processing Units (GPUs), has raised interest in a set of techniques that are able to …
A Source Identification Problem in Magnetics Solved by Means of Deep Learning Methods
In this study, a deep learning-based approach is used to address inverse problems involving
the inversion of a magnetic field and the identification of the relevant source, given the field …
the inversion of a magnetic field and the identification of the relevant source, given the field …
Synthesis of Boundary Conditions in Polygonal Magnetic Domains Using Deep Neural Networks
In this paper, the authors approach the problem of boundary condition synthesis (also
defined as field continuation) in a doubly connected domain by the use of a Neural Network …
defined as field continuation) in a doubly connected domain by the use of a Neural Network …
Synthesis of Boundary Conditions in Magnetics: a Neural Network Approach
In the paper, the synthesis of boundary conditions in a doubly-connected domain is
accomplished by means of a single-layer neural network. In this way, the classical problem …
accomplished by means of a single-layer neural network. In this way, the classical problem …