Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing
Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …
and deep learning to advance scientific computing in many fields, including fluid mechanics …
Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
Review of computational mechanics, optimization, and machine learning tools for digital twins applied to infrastructures
GE Stavroulakis, BG Charalambidi, P Koutsianitis - Applied Sciences, 2022 - mdpi.com
This review discusses the links between the newly introduced concepts of digital twins and
more classical finite element modeling, reduced order models, parametric modeling, inverse …
more classical finite element modeling, reduced order models, parametric modeling, inverse …
Physics-Informed Machine Learning—An Emerging Trend in Tribology
Physics-informed machine learning (PIML) has gained significant attention in various
scientific fields and is now emerging in the area of tribology. By integrating physics-based …
scientific fields and is now emerging in the area of tribology. By integrating physics-based …
On the order of derivation in the training of physics-informed neural networks: case studies for non-uniform beam structures
The potential of the mixed formulation for physics-informed neural networks is investigated
in order to find a solution for a non-uniform beam resting on an elastic foundation and …
in order to find a solution for a non-uniform beam resting on an elastic foundation and …
Ensemble of physics-informed neural networks for solving plane elasticity problems with examples
Two-dimensional (plane) elasticity equations in solid mechanics are solved numerically with
the use of an ensemble of physics-informed neural networks (PINNs). The system of …
the use of an ensemble of physics-informed neural networks (PINNs). The system of …
Exploring energy minimization to model strain localization as a strong discontinuity using Physics Informed Neural Networks
We explore the possibilities of using energy minimization for the numerical modeling of
strain localization in solids as a sharp discontinuity in the displacement field. For this …
strain localization in solids as a sharp discontinuity in the displacement field. For this …
Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences
Recently developed Machine Learning (ML) interpretability techniques have the potential to
explain how predictors influence the dependent variable in high-dimensional and non-linear …
explain how predictors influence the dependent variable in high-dimensional and non-linear …
An improved physical information network for forecasting the motion response of ice floes under waves
X Peng, C Wang, G **a, F Han, Z Liu, W Zhao… - Physics of …, 2024 - pubs.aip.org
Physics-informed neural networks (PINNs) have increasingly become a key intelligent
technology for solving partial differential equations. Nevertheless, for simulating the dynamic …
technology for solving partial differential equations. Nevertheless, for simulating the dynamic …
[PDF][PDF] Second-order analysis of beam-columns by machine learning-based structural analysis through physics-informed neural networks
Numerical solutions using machine learning have seen drastic development in recent years,
largely due to the advancement in Artificial Intelligence (AI) chips, especially the advent of …
largely due to the advancement in Artificial Intelligence (AI) chips, especially the advent of …