Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression (Q6185193)

scientific article; zbMATH DE number 7796670

Language	Label	Description	Also known as
English	Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression	scientific article; zbMATH DE number 7796670

Statements

instance of

scholarly article

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title

Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression (English)

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George Em. Karniadakis

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published in

Computer Methods in Applied Mechanics and Engineering

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publication date

29 January 2024

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full work available at URL

https://arxiv.org/abs/2307.08107

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zbMATH Keywords

Alzheimer's disease

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misfolded tau protein

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model discovery

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uncertainty quantification

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PINNs

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symbolic regression

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MaRDI profile type

MaRDI publication profile

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cites work

Finite bandwidth, finite amplitude convection

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Distant side-walls cause slow amplitude modulation of cellular convection

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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A new family of constitutive artificial neural networks towards automated model discovery

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Automated model discovery for skin: discovering the best model, data, and experiment

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DeepXDE: A Deep Learning Library for Solving Differential Equations

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

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Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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fPINNs: Fractional Physics-Informed Neural Networks

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Physics-informed neural networks for high-speed flows

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Multi-Element Generalized Polynomial Chaos for Arbitrary Probability Measures

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Algorithm 778: L-BFGS-B

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Solving free-surface problems for non-shallow water using boundary and initial conditions-free physics-informed neural network (bif-PINN)

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Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons

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On Estimation of a Probability Density Function and Mode

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Loss landscapes and optimization in over-parameterized non-linear systems and neural networks

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Identifiers

arXiv ID

2307.08107

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DOI

10.1016/j.cma.2023.116647

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6185193