Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios
From MaRDI portal
Publication:2683433
DOI10.1016/j.cma.2022.115852OpenAlexW4313531672MaRDI QIDQ2683433
Yong Yuan, Ba Trung Cao, Chen Xu, Günther Meschke
Publication date: 10 February 2023
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2205.07731
inverse analysismulti-task learningtransfer learningphysics-informed neural networks (PINNs)tunnel engineering
Related Items (3)
Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations ⋮ Adversarial deep energy method for solving saddle point problems involving dielectric elastomers ⋮ Data-driven wave solutions of (2+1)-dimensional nonlinear dispersive long wave equation by deep learning
Uses Software
Cites Work
- Adam
- PDE-Net
- Learning constitutive relations from indirect observations using deep neural networks
- Meta-modeling game for deriving theory-consistent, microstructure-based traction-separation laws via deep reinforcement learning
- Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning
- Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
- PPINN: parareal physics-informed neural network for time-dependent PDEs
- Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
- Smart constitutive laws: inelastic homogenization through machine learning
- Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network
- SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
- The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
- Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
- Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method
- Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning
- nPINNs: nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- Transfer learning based multi-fidelity physics informed deep neural network
- NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
- Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
- The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
- When and why PINNs fail to train: a neural tangent kernel perspective
- Physics informed neural networks for continuum micromechanics
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
- CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method
- Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
- Scientific machine learning through physics-informed neural networks: where we are and what's next
- Physics-informed PointNet: a deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture
- PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
- Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
- A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches
- On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks
- Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
- A nonlocal physics-informed deep learning framework using the peridynamic differential operator
- A deep energy method for finite deformation hyperelasticity
- Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
- A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
- A neural network-based surrogate model for carbon nanotubes with geometric nonlinearities
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
- A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Bayesian physics informed neural networks for real-world nonlinear dynamical systems
- Scaling of Differential Equations
- Machine Learning for Fluid Mechanics
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
- Solving high-dimensional partial differential equations using deep learning
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
- Turbulence Modeling in the Age of Data
This page was built for publication: Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios
