AIMC Topic: Physics

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[Nobel Prize in physics 2024 : John J. Hopfield and Geoffrey E. Hinton. From Hopfield and Hinton to AlphaFold: The 2024 Nobel Prize honors the pioneers of deep learning].

Medecine sciences : M/S Mar 21, 2025
On October 8, 2024, the Nobel Prize in Physics was awarded to John J. Hopfield, professor at Princeton University, and Geoffrey E. Hinton, professor at the University of Toronto, for their "fundamental discoveries that made possible machine learning ...
Humans History, 20th Century Physics Nobel Prize Deep Learning History, 21st Century
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Hypernetwork-Based Physics-Driven Personalized Federated Learning for CT Imaging.

IEEE transactions on neural networks and learning systems Feb 6, 2025
In clinical practice, computed tomography (CT) is an important noninvasive inspection technology to provide patients' anatomical information. However, its potential radiation risk is an unavoidable problem that raises people's concerns. Recently, dee...
Neural Networks, Computer Algorithms Tomography, X-Ray Computed Federated Learning Deep Learning Image Processing, Computer-Assisted Humans Physics
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Physics-informed Neural Implicit Flow neural network for parametric PDEs.

Neural networks : the official journal of the International Neural Network Society Jan 16, 2025
The Physics-informed Neural Network (PINN) has been a popular method for solving partial differential equations (PDEs) due to its flexibility. However, PINN still faces challenges in characterizing spatio-temporal correlations when solving parametric...
Physics Humans Neural Networks, Computer Algorithms Nonlinear Dynamics
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High-resolution hemodynamic estimation from ultrafast ultrasound image velocimetry using a physics-informed neural network.

Physics in medicine and biology Jan 9, 2025
Estimating the high-resolution (HR) blood flow velocity and pressure fields for the diagnosis and treatment of vascular diseases remains challenging.. In this study, a physics-informed neural network (PINN) with a refined mapping capability was combi...
Physics Physical Phenomena Rheology Blood Flow Velocity Hemodynamics Time Factors Ultrasonography Neural Networks, Computer Image Processing, Computer-Assisted Animals Humans
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A simple remedy for failure modes in physics informed neural networks.

Neural networks : the official journal of the International Neural Network Society Dec 10, 2024
Physics-informed neural networks (PINNs) have shown promising results in solving a wide range of problems involving partial differential equations (PDEs). Nevertheless, there are several instances of the failure of PINNs when PDEs become more complex...
Algorithms Computer Simulation Nonlinear Dynamics Physics Neural Networks, Computer
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An extrapolation-driven network architecture for physics-informed deep learning.

Neural networks : the official journal of the International Neural Network Society Dec 5, 2024
Current physics-informed neural network (PINN) implementations with sequential learning strategies often experience some weaknesses, such as the failure to reproduce the previous training results when using a single network, the difficulty to strictl...
Deep Learning Neural Networks, Computer Algorithms Physics
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Diminishing spectral bias in physics-informed neural networks using spatially-adaptive Fourier feature encoding.

Neural networks : the official journal of the International Neural Network Society Nov 18, 2024
Physics-informed neural networks (PINNs) have recently emerged as a promising framework for solving partial differential equation (PDE) systems in computer mechanics. However, PINNs still struggle in simulating systems whose solution functions exhibi...
Physics Computer Simulation Fourier Analysis Neural Networks, Computer Algorithms
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Exactly conservative physics-informed neural networks and deep operator networks for dynamical systems.

Neural networks : the official journal of the International Neural Network Society Oct 22, 2024
We introduce a method for training exactly conservative physics-informed neural networks and physics-informed deep operator networks for dynamical systems, that is, for ordinary differential equations. The method employs a projection-based technique ...
Neural Networks, Computer Algorithms Deep Learning Physics
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Deep fuzzy physics-informed neural networks for forward and inverse PDE problems.

Neural networks : the official journal of the International Neural Network Society Oct 15, 2024
As a grid-independent approach for solving partial differential equations (PDEs), Physics-Informed Neural Networks (PINNs) have garnered significant attention due to their unique capability to simultaneously learn from both data and the governing phy...
Algorithms Computer Simulation Humans Neural Networks, Computer Physics Deep Learning Fuzzy Logic
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A physics-informed deep learning framework for dynamic susceptibility contrast perfusion MRI.

Medical physics Sep 20, 2024
BACKGROUND: Perfusion magnetic resonance imaging (MRI)s plays a central role in the diagnosis and monitoring of neurovascular or neurooncological disease. However, conventional processing techniques are limited in their ability to capture relevant ch...
Cerebrovascular Circulation Glioma Deep Learning Physics Contrast Media Image Processing, Computer-Assisted Humans Magnetic Resonance Imaging Brain Neoplasms Magnetic Resonance Angiography
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