用物理约束的函数链接法分析指数载荷下变截面带孔梁弯曲,精度更高、更快收敛。
Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beam
- 用正交多项式扩展输入,通过TFC构造满足边界条件的约束表达式
- 相比PINN,收敛更快、计算成本更低,误差更小
- 适合求解含复杂几何与载荷的微分方程工程问题
本文提出一种新颖的域映射物理信息函数链接框架(DFL-TFC),用于分析指数载荷下变截面带孔梁的弯曲行为。控制微分方程包含填充率(α)、孔洞排数(N)、锥度参数(ϕ, ψ)和指数载荷参数(γ),可灵活表征复杂结构。该方法将隐层替换为基于正交多项式基函数的功能扩展块,并将微分方程定义域映射至正交多项式对应域。利用理论功能连接(TFC)构建约束表达式(CE),精确满足边界条件;其中自由函数由函数链接神经网络(FLNN)表示,学习求解无约束优化问题。结果表明,该框架相较基于PINN的模型具有更优性能:收敛速度更快、计算成本更低、解的精度更高。数值验证通过伽辽金法和PINN解完成,进一步确认了方法的有效性。
原文摘要 · Abstract (English)
This article presents a novel and comprehensive approach for analyzing bending behavior of the tapered perforated beam under an exponential load. The governing differential equation includes important factors like filling ratio ($α$), number of rows of holes ($N$), tapering parameters ($ϕ$ and $ψ$), and exponential loading parameter ($γ$), providing a realistic and flexible representation of perforated beam configuration. Main goal of this work is to see how well the Domain mapped physics-informed Functional link Theory of Functional Connection (DFL-TFC) method analyses bending response of perforated beam with square holes under exponential loading. For comparison purposes, a corresponding PINN-based formulation is developed. Outcomes clearly show that the proposed DFL-TFC framework gives better results, including faster convergence, reduced computational cost, and improved solution accuracy when compared to the PINN approach. These findings highlight effectiveness and potential of DFL-TFC method for solving complex engineering problems governed by differential equations. Within this framework, hidden layer is replaced by a functional expansion block that enriches input representation via orthogonal polynomial basis functions, and the domain of DE mapped to corresponding domain of orthogonal polynomials. A Constrained Expression (CE), constructed through the Theory of Functional Connections (TFC) using boundary conditions, ensures that constraints are exactly satisfied. In CE, free function is represented using a Functional Link Neural Network (FLNN), which learns to solve resulting unconstrained optimization problem. The obtained results are further validated through the Galerkin and PINN solutions.
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