Finite Element Model
Finite Element Model (FEM) is a numerical method that approximates the physical behavior of an entity by discretizing a continuous physical structure into a finite number of small parts, namely "elements". These elements can be one-dimensional line elements, two-dimensional surface elements, or three-dimensional solid elements. Each element has a simple geometric shape and is connected by nodes. In this way, the finite element model is able to simulate physical phenomena such as stress, strain, temperature, fluid flow, etc. in complex structures. In its most basic form, FEM is an approximation method that approximates a complex problem space ordomainSubdivided into many small, simpler parts (finite elements) whose behavior can be described by relatively simple equations.
An important aspect of FEM is how to subdivide the domain. Computer-aided design (CAD) software is helpful in this regard because it defines the 3D shape of the object and can easily subdivide the object into appropriately sized elements based on the desired mesh or 3D grid that defines the elements..Depending on the problem being solved, the mesh can define elements of uniform size and shape (such as cubes or pyramids), or it can have elements of different shapes and sizes in different parts of the domain.
Finite Element Models in Artificial Intelligence
Finite Element Model (FEM) in the field of artificial intelligence usually refers to a computational model that combines finite element analysis (FEA) with artificial intelligence technologies (such as machine learning and deep learning). This model uses finite element analysis to numerically simulate physical phenomena, and combines artificial intelligence algorithms to optimize, predict or support decision-making of model parameters. Finite element models can be applied to various engineering problems, such as structural analysis, thermal analysis, fluid dynamics, etc., and their efficiency and accuracy can be improved through artificial intelligence technology.
In artificial intelligence, the applications of finite element models include but are not limited to:
- Parameter Optimization: Use machine learning algorithms to optimize the parameters of finite element models to improve the accuracy of simulations or reduce computational costs.
- Model predictions: Train deep learning models to predict engineering performance or life based on finite element analysis data.
- Inverse problem solving: Use artificial intelligence techniques to solve inverse problems in finite element models, such as crack detection or material property inversion.
- Adaptive Mesh Refinement: Automatically adjust the grid density based on artificial intelligence algorithms to improve calculation efficiency and result accuracy.