[HTML][HTML] Physics-informed machine learning: a comprehensive review on applications in anomaly detection and condition monitoring
Condition monitoring plays a vital role in ensuring the reliability and optimal performance of
various engineering systems. Traditional methods for condition monitoring rely on physics …
various engineering systems. Traditional methods for condition monitoring rely on physics …
Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design
Triboelectric nanogenerators offer an environmentally friendly approach to harvesting
energy from mechanical excitations. This capability has made them widely sought‐after as …
energy from mechanical excitations. This capability has made them widely sought‐after as …
Machine learning-based morphological and mechanical prediction of kirigami-inspired active composites
Kirigami-inspired designs hold great potential for the development of functional materials
and devices, but predicting the morphological configuration of these structures under …
and devices, but predicting the morphological configuration of these structures under …
Uncertainty quantification of microstructures: a perspective on forward and inverse problems for mechanical properties of aerospace materials
In this review, state‐of‐the‐art studies on the uncertainty quantification (UQ) of
microstructures in aerospace materials is examined, addressing both forward and inverse …
microstructures in aerospace materials is examined, addressing both forward and inverse …
PICProp: physics-informed confidence propagation for uncertainty quantification
Standard approaches for uncertainty quantification in deep learning and physics-informed
learning have persistent limitations. Indicatively, strong assumptions regarding the data …
learning have persistent limitations. Indicatively, strong assumptions regarding the data …
Mixed form based physics-informed neural networks for performance evaluation of two-phase random materials
X Ren, X Lyu - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Abstract Evaluation of material performance is crucial in establishing processing-structure-
property (PSP) relationships in material design. Finite element method (FEM) is commonly …
property (PSP) relationships in material design. Finite element method (FEM) is commonly …
Solving forward and inverse problems of contact mechanics using physics-informed neural networks
This paper explores the ability of physics-informed neural networks (PINNs) to solve forward
and inverse problems of contact mechanics for small deformation elasticity. We deploy …
and inverse problems of contact mechanics for small deformation elasticity. We deploy …
Towards Solving Industry-Grade Surrogate Modeling Problems using Physics Informed Machine Learning
S Bhatnagar, A Comerford, A Banaeizadeh - arXiv preprint arXiv …, 2023 - arxiv.org
Deep learning combined with physics-based modeling represents an attractive and efficient
approach for producing accurate and robust surrogate modeling. In this paper, a new …
approach for producing accurate and robust surrogate modeling. In this paper, a new …
Physics Informed Neural Networks for Modeling of 3D Flow-Thermal Problems with Sparse Domain Data
S Bhatnagar, A Comerford… - Journal of Machine …, 2024 - dl.begellhouse.com
ABSTRACT Successfully training Physics Informed Neural Networks (PINNs) for highly
nonlinear PDEs on complex 3D domains remains a challenging task. In this paper, PINNs …
nonlinear PDEs on complex 3D domains remains a challenging task. In this paper, PINNs …
Applications of physics-informed neural networks for property characterization of complex materials
S Lee, J Popovics - RILEM Technical Letters, 2022 - letters.rilem.net
The characterization of in-place material properties is important for quality control and
condition assessment of the built infrastructure. Although various methods have been …
condition assessment of the built infrastructure. Although various methods have been …