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Robust Inverse Material Parameter Identification based on Physics-Informed Neural Networks

https://doi.org/10.2139/ssrn.5497567
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The Mathematical Theory of Finite Element Methods
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On the limited memory BFGS method for large scale optimization
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On the order of derivation in the training of physics-informed neural networks: case studies for non-uniform beam structures
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Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics
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From PINNs to PIKANs: recent advances in physics-informed machine learning
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A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
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A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
inferredcites →https://doi.org/10.1016/j.engappai.2023.105828openalex-snapshot
Constitutive model characterization and discovery using physics-informed deep learning
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Comparison of inverse identification strategies for constitutive mechanical models using full-field measurements
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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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NN-EUCLID: Deep-learning hyperelasticity without stress data
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Learning in PINNs: Phase transition, diffusion equilibrium, and generalization
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Discovering plasticity models without stress data
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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
inferredcites →https://doi.org/10.1109/access.2024.3465572openalex-snapshot
Sobolev Neural Network With Residual Weighting as a Surrogate in Linear and Non-Linear Mechanics
inferredcites →https://doi.org/10.1111/str.12134openalex-snapshot
Effect of DIC Spatial Resolution, Noise and Interpolation Error on Identification Results with the VFM
inferredcites →https://doi.org/10.1111/str.12370openalex-snapshot
Towards Material Testing 2.0. A review of test design for identification of constitutive parameters from full‐field measurements
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Material Testing 2.0: A brief review
inferredcites →https://doi.org/10.1111/str.12473openalex-snapshot
Metrics to evaluate constitutive model fitness based on DIC experiments
inferredcites →https://doi.org/10.1137/19m1274067openalex-snapshot
DeepXDE: A Deep Learning Library for Solving Differential Equations
inferredcites →https://doi.org/10.2139/ssrn.5261377openalex-snapshot
Which Optimizer Works Best for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks?
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.2308.08468openalex-snapshot
An Expert's Guide to Training Physics-informed Neural Networks
inferredcites →https://doi.org/10.52202/075280-1032openalex-snapshot
Separable Physics-Informed Neural Networks
inferredcites →https://doi.org/10.5281/zenodo.1287831openalex-snapshot
Numerical Tours of Computational Mechanics with FEniCS
inferredcites →https://doi.org/10.5281/zenodo.7566340openalex-snapshot
Accuracy of physics informed neural network used to model the elastic response of a material
inferredcites →https://doi.org/10.7712/120223.10370.19940openalex-snapshot
QUANTIFYING UNCERTAINTY OF PHYSICS-INFORMED NEURAL NETWORKS FOR CONTINUUM MECHANICS APPLICATIONS
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