Deep learning of free boundary and Stefan problems (Q2128318)

scientific article; zbMATH DE number 7511408

Language	Label	Description	Also known as
English	Deep learning of free boundary and Stefan problems	scientific article; zbMATH DE number 7511408

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instance of

scholarly article

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title

Deep learning of free boundary and Stefan problems (English)

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Journal of Computational Physics

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publication date

21 April 2022

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full work available at URL

https://arxiv.org/abs/2006.05311

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zbMATH Keywords

keywords physics-informed neural networks

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phase transitions

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partial differential equations

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scientific machine learning

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describes a project that uses

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Free boundary problems in science and technology.

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Solidification of droplets on a cold surface

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Q4849755

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Optimal stopping, free boundary, and American option in a jump-diffusion model

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A bibliography on moving-free boundary problems for the heat-diffusion equation. The Stefan and related problems

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A Comparative Study of Numerical Methods for Moving Boundary Problems

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A Front-Tracking Method for Multiphase Free Boundary Problems

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A front-tracking method for viscous, incompressible multi-fluid flows

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An implicit enthalpy solution for phase change problems: with application to a binary alloy solidification

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A moving mesh method for the solution of the one-dimensional phase-field equations

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A simple level set method for solving Stefan problems

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A comparison of numerical models for one-dimensional Stefan problems

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DGM: a deep learning algorithm for solving partial differential equations

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The numerical solution of the inverse Stefan problem

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The Numerical Solution of the Inverse Stefan Problem in Two Space Variables

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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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fPINNs: Fractional Physics-Informed Neural Networks

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

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PPINN: parareal physics-informed neural network for time-dependent PDEs

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Solving high-dimensional partial differential equations using deep learning

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Adversarial uncertainty quantification in physics-informed neural networks

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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

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Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

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A method for the numerical solution of the one-dimensional inverse Stefan problem

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Solving the inverse Stefan design problem using genetic algorithms

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A method of fundamental solutions for the one-dimensional inverse Stefan problem

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An adaptive finite element method for two-phase Stefan problems in two space dimensions. I. Stability and error estimates

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A meshless method for an inverse two-phase one-dimensional linear Stefan problem

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Numerical computation of the free boundary for the two-dimensional Stefan problem by space-time finite elements

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An ALE finite element method for a coupled Stefan problem and Navier–Stokes equations with free capillary surface

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A Stefan Problem for a Protocell Model

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Non-invasive inference of thrombus material properties with physics-informed neural networks

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A free boundary problem arising in a model of wound healing

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Data driven governing equations approximation using deep neural networks

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Data-driven deep learning of partial differential equations in modal space

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Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.JCP.2020.109914

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Sitelinks

Mathematics(1 entry)

mardi Publication:2128318