Bayesian inversion with neural operator (BINO) for modeling subdiffusion: forward and inverse problems (Q6593344)

scientific article; zbMATH DE number 7901824

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English	Bayesian inversion with neural operator (BINO) for modeling subdiffusion: forward and inverse problems	scientific article; zbMATH DE number 7901824

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instance of

scholarly article

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title

Bayesian inversion with neural operator (BINO) for modeling subdiffusion: forward and inverse problems (English)

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Journal of Computational and Applied Mathematics

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publication date

26 August 2024

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zbMATH Keywords

anomalous diffusion

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operator learning

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Bayesian inverse problems

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MaRDI profile type

Publication

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cites work

Stochastic modeling in nanoscale biophysics: subdiffusion within proteins

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An Explicit Finite Difference Method and a New von Neumann-Type Stability Analysis for Fractional Diffusion Equations

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The Use of Finite Difference/Element Approaches for Solving the Time-Fractional Subdiffusion Equation

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A Fourier method for the fractional diffusion equation describing sub-diffusion

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Correction of High-Order BDF Convolution Quadrature for Fractional Evolution Equations

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Numerical methods for time-fractional evolution equations with nonsmooth data: a concise overview

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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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Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains

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Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions

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Weak adversarial networks for high-dimensional partial differential equations

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Numerical solution of inverse problems by weak adversarial networks

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fPINNs: Fractional Physics-Informed Neural Networks

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Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations

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Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

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Prediction of aerodynamic flow fields using convolutional neural networks

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MIONet: Learning Multiple-Input Operators via Tensor Product

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DeepM\&Mnet for hypersonics: predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators

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MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs

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Initial value/boundary value problems for fractional diffusion-wave equations and applications to some inverse problems

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Fractional differential equations. An introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications

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A fully discrete difference scheme for a diffusion-wave system

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Bayesian approach to a nonlinear inverse problem for a time-space fractional diffusion equation

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Identify the fractional order and diffusion coefficient in a fractional diffusion wave equation

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MCMC methods for functions: modifying old algorithms to make them faster

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An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems

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Identifiers

zbMATH Open document ID

1547.65132

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DOI

10.1016/j.cam.2024.116191

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6593344