# Physics-Informed Neural Networks for Nonlocal Beam Eigenvalue Problems Source: [Physics-Informed Neural Networks for Nonlocal Beam Eigenvalue Problems]() Collection index: [llms.txt]() Data updated: 2026-01-17T05:00:11.890Z Authors: Baidehi Das, Raffaele Barretta, Marko \\v{C}ana{\\dj}ija Published: 2025-09-04T15:41:25.000Z ## Abstract The present study investigates the dynamics of nonlocal beams by establishing a consistent stress-driven integral elastic using the Physics-Informed Neural Network (PINN) approach. Specifically, a PINN is developed to compute the first eigenfunction and eigenvalue arising from the underlying sixth-order ordinary differential equation. The PINN is based on a feedforward neural network, with a loss function composed of terms from the differential equation, the normalization condition, and both boundary and constitutive boundary conditions. Relevant eigenvalues are treated as separate trainable variables. The results demonstrate that the proposed method is a powerful and robust tool for addressing the complexity of the problem. Once trained, the neural network is less computationally intensive than analytical methods. The obtained results are compared with benchmark analytical solutions and show strong agreement. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Referring Expression Comprehension on OpenRef](): Referring Expression Comprehension; OpenRef; F1 (Landmark): 98.5. - [Referring Expression Comprehension on RefCOCO (F1 Average)](): Referring Expression Comprehension; RefCOCO; F1 (RefCOCO): 87.2. ## Original sources and implementation [Original paper]() [PDF]()