Physical informed neural network for thermo-hydral analysis of fire-loaded concrete (Q6566857)

scientific article; zbMATH DE number 7875811

Language	Label	Description	Also known as
English	Physical informed neural network for thermo-hydral analysis of fire-loaded concrete	scientific article; zbMATH DE number 7875811

Statements

instance of

scholarly article

0 references

title

Physical informed neural network for thermo-hydral analysis of fire-loaded concrete (English)

0 references

0 references

0 references

0 references

0 references

0 references

Engineering Analysis with Boundary Elements

0 references

publication date

3 July 2024

0 references

zbMATH Keywords

physical informed neural network (PINN)

0 references

concrete under fire

0 references

multifield analysis

0 references

spalling

0 references

numerical simulation

0 references

risk assessment

0 references

MaRDI profile type

Publication

0 references

cites work

Learning constitutive relations from indirect observations using deep neural networks

0 references

A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning

0 references

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

0 references

Q4558473

0 references

Physics-informed neural networks for high-speed flows

0 references

An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications

0 references

Parametric deep energy approach for elasticity accounting for strain gradient effects

0 references

DGM: a deep learning algorithm for solving partial differential equations

0 references

Physics informed neural networks for continuum micromechanics

0 references

Physics-informed deep learning for melting heat transfer analysis with model-based transfer learning

0 references

DeepXDE: A Deep Learning Library for Solving Differential Equations

0 references

SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks

0 references

A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations

0 references

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

0 references

Modelling of hygro-thermal behaviour of concrete at high temperature with thermo-chemical and mechanical material degradation.

0 references

Thermo-hydro-mechanical analysis of partially saturated porous materials

0 references

Thermo‐hydro‐mechanical modelling of high performance concrete at high temperatures

0 references

Multiphase flow in deforming porous material

0 references

Towards prediction of the thermal spalling risk through a multi-phase porous media model of concrete

0 references

Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training

0 references

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

0 references

A deep energy method for finite deformation hyperelasticity

0 references

On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs

0 references

Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture

0 references

Pre-training strategy for solving evolution equations based on physics-informed neural networks

0 references

A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations

0 references

Predicting the Pore-Pressure and Temperature of Fire-Loaded Concrete by a Hybrid Neural Network

0 references

Identifiers

Mathematics Subject Classification ID

0 references

0 references

0 references

10.1016/J.ENGANABOUND.2023.10.027

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:6566857