AIMC Topic: Physics

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Kolmogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics.

Neural networks : the official journal of the International Neural Network Society Sep 4, 2024
Physics-informed machine learning (PIML) as a means of solving partial differential equations (PDEs) has garnered much attention in the Computational Science and Engineering (CS&E) world. This topic encompasses a broad array of methods and models aim...
Algorithms Physics Neural Networks, Computer Machine Learning
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Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.

Magma (New York, N.Y.) May 14, 2024
OBJECT: To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability.
Coronary Vessels Physics Image Processing, Computer-Assisted Magnetic Resonance Imaging Neural Networks, Computer Algorithms Deep Learning Imaging, Three-Dimensional Humans
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Tackling the curse of dimensionality with physics-informed neural networks.

Neural networks : the official journal of the International Neural Network Society May 7, 2024
The curse-of-dimensionality taxes computational resources heavily with exponentially increasing computational cost as the dimension increases. This poses great challenges in solving high-dimensional partial differential equations (PDEs), as Richard E...
Physics Stochastic Processes Neural Networks, Computer Algorithms Computer Simulation Nonlinear Dynamics
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Towards complex dynamic physics system simulation with graph neural ordinary equations.

Neural networks : the official journal of the International Neural Network Society Apr 25, 2024
The great learning ability of deep learning facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour both in academia and industry. However, the complex laws of the physical ...
Deep Learning Gravitation Physics Algorithms Computer Simulation Neural Networks, Computer
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Physics-informed neural wavefields with Gabor basis functions.

Neural networks : the official journal of the International Neural Network Society Apr 8, 2024
Recently, Physics-Informed Neural Networks (PINNs) have gained significant attention for their versatile interpolation capabilities in solving partial differential equations (PDEs). Despite their potential, the training can be computationally demandi...
Nonlinear Dynamics Physics Algorithms Neurons Neural Networks, Computer
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Physics-assisted machine learning for THz time-domain spectroscopy: sensing leaf wetness.

Scientific reports Mar 25, 2024
Signal processing techniques are of vital importance to bring THz spectroscopy to a maturity level to reach practical applications. In this work, we illustrate the use of machine learning techniques for THz time-domain spectroscopy assisted by domain...
Plant Leaves Spectrum Analysis Physics Machine Learning Water
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AI-Aristotle: A physics-informed framework for systems biology gray-box identification.

PLoS computational biology Mar 12, 2024
Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation and missing physics identificati...
Neural Networks, Computer Physics Machine Learning Systems Biology
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Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers.

Journal of chemical information and modeling Mar 1, 2024
Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being ...
Polymers Polyesters Stereoisomerism Physics Deep Learning
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Performance of Fourier-based activation function in physics-informed neural networks for patient-specific cardiovascular flows.

Computer methods and programs in biomedicine Feb 22, 2024
BACKGROUND AND OBJECTIVES: Physics-informed neural networks (PINNs) can be used to inversely model complex physical systems by encoding the governing partial differential equations and training data into the neural network. However, neural networks a...
Diffusion Neural Networks, Computer Physics Aorta Constriction, Pathologic Humans
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Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.

Molecules (Basel, Switzerland) Feb 13, 2024
The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sa...
Protein Binding Proteins Retrospective Studies Binding Sites Thermodynamics Ligands Cost-Benefit Analysis Machine Learning Physics
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