Search Results for author:
Found 14 papers, 3 papers with code
HomoGenius: a Foundation Model of Homogenization for Rapid Prediction of Effective Mechanical Properties using Neural Operators
Homogenization is an essential tool for studying multiscale physical phenomena.
DCEM-PINNs: A deep complementary energy method for solid mechanics
The results demonstrate that DCEM outperforms DEM in terms of stress accuracy and efficiency and has an advantage in dealing with complex displacement boundary conditions, which is supported by theoretical analyses and numerical simulations.
Multigoal-oriented dual-weighted-residual error estimation using deep neural networks
An efficient and easy to implement algorithm is developed to obtain a posteriori error estimate for multiple goal functionals by employing the dual-weighted residual approach, which is followed by the computation of both primal and adjoint solutions using the neural network.
Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows
In this paper, a novel deep learning framework is proposed for temporal super-resolution simulation of blood vessel flows, in which a high-temporal-resolution time-varying blood vessel flow simulation is generated from a low-temporal-resolution flow simulation result.
A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate
In this paper, a deep collocation method (DCM) for thin plate bending problems is proposed.
A 55-line code for large-scale parallel topology optimization in 2D and 3D
This paper presents a 55-line code written in python for 2D and 3D topology optimization (TO) based on the open-source finite element computing software (FEniCS), equipped with various finite element tools and solvers.
Mathematical Software Computational Engineering, Finance, and Science Optimization and Control
Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates
In this paper, we present a deep autoencoder based energy method (DAEM) for the bending, vibration and buckling analysis of Kirchhoff plates.
Analysis of three dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis
This approach utilizes a physics informed neural network with material transfer learning reducing the solution of the nonhomogeneous partial differential equations to an optimization problem.
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning
In this work, a modified neural architecture search method (NAS) based physics-informed deep learning model is presented for stochastic analysis in heterogeneous porous material.
High correlated variables creator machine: Prediction of the compressive strength of concrete
HCVCM improves the accuracy of ANFIS by 5% in the coefficient of determination, 10% in RMSE, 3% in NMSE, 20% in MAPE, and 7% in the maximum negative error.
Shear Stress Distribution Prediction in Symmetric Compound Channels Using Data Mining and Machine Learning Models
Finally, the most powerful data mining method which studied in this research (RF) compared with two well-known analytical models of Shiono and Knight Method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution.
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications
In this contribution, we explore Deep Neural Networks (DNNs) as an option for approximation.
Transfer learning enhanced physics informed neural network for phase-field modeling of fracture
While most of the PINN algorithms available in the literature minimize the residual of the governing partial differential equation, the proposed approach takes a different path by minimizing the variational energy of the system.
Particle swarm optimization model to predict scour depth around bridge pier
To improve the efficiency of the proposed model, individual equations are derived for laboratory and field data.
