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Found 7 papers, 3 papers with code
A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations
Our method, inspired by operator learning and the finite element method, demonstrates the ability to train without relying on data from other numerical solvers.
Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations
We benchmark our methodology against the standard finite element method, demonstrating accurate yet faster predictions using the trained neural network for temperature and flux profiles.
Learning solution of nonlinear constitutive material models using physics-informed neural networks: COMM-PINN
In order to demonstrate the applicability of the methodology in handling complex path dependency in a three-dimensional (3D) scenario, we tested the approach using the equations governing a damage model for a three-dimensional interface model.
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains
In this work, we propose applying the mixed formulation to solve multi-physical problems, specifically a stationary thermo-mechanically coupled system of equations.
AI enhanced finite element multiscale modelling and structural uncertainty analysis of a functionally graded porous beam
The error range of CNN models leads to an uncertain mechanical performance, which is further evaluated in a structural uncertainty analysis on the FG porous three-layer beam consisting of two thin high-density layers and a thick low-density one, where the imprecise CNN predicted moduli are represented as triangular fuzzy numbers in double parametric form.
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method
Later on, the strong form which has a higher order of derivatives is applied to the spatial gradients of the primary variable as the physical constraint.
Lossless Multi-Scale Constitutive Elastic Relations with Artificial Intelligence
To demonstrate the accuracy and the efficiency of the trained CNN model, a Finite Element Method (FEM) based result of an elastically deformed nanoporous beam equipped with the CNN as constitutive law is compared with that by a full atomistic simulation.
