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Article Citation - WoS: 2Citation - Scopus: 2Physics-Informed Neural Network for Nonlinear Bending Analysis of Nano-Beams: A Systematic Hyperparameter Optimization(MDPI, 2025) Esfahani, Saba Sadat Mirsadeghi; Fallah, Ali; Aghdam, Mohammad MohammadiThis paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton's principle, incorporating nonlocal strain gradient theory, and based on Euler-Bernoulli beam theory. In the PINN method, the solution is approximated by a deep neural network, with network parameters determined by minimizing a loss function that consists of the governing equation and boundary conditions. Despite numerous reports demonstrating the applicability of the PINN method for solving various engineering problems, tuning the network hyperparameters remains challenging. In this study, a systematic approach is employed to fine-tune the hyperparameters using hyperparameter optimization (HPO) via Gaussian process-based Bayesian optimization. Comparison of the PINN results with available reference solutions shows that the PINN, with the optimized parameters, produces results with high accuracy. Finally, the impacts of boundary conditions, different loads, and the influence of nonlocal strain gradient parameters on the bending behavior of nano-beams are investigated.Article Citation - WoS: 1Citation - Scopus: 14D-Printed Continuous Fiber-Reinforced PLA/TPU Auxetic Composites: Mechanical Performance, Energy Absorption, Shape Recovery, and Reusability Evaluation(SpringerNature, 2025) Alkan, Atakan; Ranjbar Aghjehkohal, Amin; Fallah, Ali; Koc, BahattinThis study explores the mechanical performance, energy absorption, shape recovery, and reusability of 4D-printed continuous carbon fiber-reinforced auxetic composite structures based on PLA/TPU blends, designed for load-bearing applications. PLA-TPU mixtures with different TPU content were developed to optimize the balance between flexibility and strength, with carbon fibers incorporated to enhance the mechanical properties of the resulting composites. Thermo-mechanical characterization of the blends was conducted, followed by a detailed evaluation of the structures' mechanical behavior and energy absorption capacity under room temperature conditions, simulating practical industrial scenarios. The shape recovery performance of these composite structures was also investigated. To assess reusability, the programming-recovery cycle was repeated five times, analyzing the retention of mechanical properties and shape recovery over multiple cycles to determine durability. Results revealed that TPU integration provided sufficient flexibility for cold programming, while carbon fiber reinforcement significantly enhanced stiffness and strength. The 4D-printed composites exhibited consistent shape recovery and maintained mechanical integrity after five cycles, confirming their reusability. These findings demonstrate the potential of 4D-printed PLA/TPU-based carbon fiber-reinforced composites as smart, durable materials for load-bearing applications in industries such as biomedical engineering, automotive, and aerospace.
