Artificial intelligence for partial differential equations in computational mechanics: A review
In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields,
especially integrating artificial intelligence and traditional science (AI for Science: Artificial …
especially integrating artificial intelligence and traditional science (AI for Science: Artificial …
Physics-informed graph convolutional neural network for modeling fluid flow and heat convection
This paper introduces a novel surrogate model for two-dimensional adaptive steady-state
thermal convection fields based on deep learning technology. The proposed model aims to …
thermal convection fields based on deep learning technology. The proposed model aims to …
A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines
Efficient and accurate prediction of the wind turbine dynamic wake is crucial for active wake
control and load assessment in wind farms. This paper proposes a real-time dynamic wake …
control and load assessment in wind farms. This paper proposes a real-time dynamic wake …
Deciphering and integrating invariants for neural operator learning with various physical mechanisms
Neural operators have been explored as surrogate models for simulating physical systems
to overcome the limitations of traditional partial differential equation (PDE) solvers. However …
to overcome the limitations of traditional partial differential equation (PDE) solvers. However …
Entropy-dissipation informed neural network for mckean-vlasov type pdes
Abstract The McKean-Vlasov equation (MVE) describes the collective behavior of particles
subject to drift, diffusion, and mean-field interaction. In physical systems, the interaction term …
subject to drift, diffusion, and mean-field interaction. In physical systems, the interaction term …
Immersed boundary method-incorporated physics-informed neural network for simulation of incompressible flows around immersed objects
In this work, an immersed boundary method-incorporated physics informed neural network
(IBM-PINN) is proposed to simulate steady incompressible flows around immersed objects …
(IBM-PINN) is proposed to simulate steady incompressible flows around immersed objects …
An improved physical information network for forecasting the motion response of ice floes under waves
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 …
The Decoupling Concept Bottleneck Model
The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-
level concepts to explain model decisions and conduct human-machine interaction …
level concepts to explain model decisions and conduct human-machine interaction …
Prediction of velocity and pressure of gas-liquid flow using spectrum-based physics-informed neural networks
This research introduces a spectrum-based physics-informed neural network (SP-PINN)
model to significantly improve the accuracy of calculation of two-phase flow parameters …
model to significantly improve the accuracy of calculation of two-phase flow parameters …
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
In scenarios with limited available data, training the function-to-function neural PDE solver in
an unsupervised manner is essential. However, the efficiency and accuracy of existing …
an unsupervised manner is essential. However, the efficiency and accuracy of existing …