Flexible inorganic electronic devices (FIEDs) consisting of functional inorganic components on a soft polymer substrate have enabled many novel applications such as epidermal electronics and wearable electronics, which cannot be realized through conventional rigid electronics. The low thermal dissipation capacity of the soft polymer substrate of FIEDs demands proper thermal management to reduce the undesired thermal influences. The biointegrated applications of FIEDs pose even more stringent requirements on thermal management due to the sensitive nature of biological tissues to temperature. In this review, we take microscale inorganic light-emitting diodes (μ-ILEDs) as an example of functional components to summarize the recent advances on thermal management of FIEDs including thermal analysis, thermo-mechanical analysis and thermal designs of FIEDs with and without biological tissues. These results are very helpful to understand the underlying heat transfer mechanism and provide design guidelines to optimize FIEDs in practical applications.

The problem that the thermal safety of flexible electronic devices is difficult to evaluate in real time is addressed in this study by establishing a BP neural network (GA-BPNN) temperature prediction model based on genetic algorithm optimisation. The model uses a BP neural network to fit the functional relationship between the input condition and the steady-state temperature of the equipment and uses a genetic algorithm to optimise the parameter initialisation problem of the BP neural network. To overcome the challenge of the high cost of obtaining experimental data, finite element analysis software is used to simulate the temperature results of the equipment under different working conditions. The prediction variance of the GA-BPNN model does not exceed 0.57 °C and has good robustness, as the model is trained according to the simulation data. The study conducted thermal validation experiments on the temperature prediction model for this flexible electronic device. The device reached steady state after 1200 s of operation at rated power. The error between the predicted and experimental results was less than 0.9 °C, verifying the validity of the model’s predictions. Compared with traditional thermal simulation and experimental methods, this model can quickly predict the temperature with a certain accuracy and has outstanding advantages in computational efficiency and integrated application of hardware and software.

Skin pain resulting from mechanical compression is one of the most common pains in daily life and the indispensable information for electronic skin to perceive external signals. The external mechanical stimuli are transduced into impulses and transmitted via nerve fiber, and finally, the sensation is perceived via the procession of the nerve system. However, the mathematical mechanism for pain sensation due to mechanical stimuli remains unclear. In this paper, a mathematical model for skin pain sensation under compression is established, in which the Flament solution, the revised Hodgkin–Huxley model, and the mathematical model gate control theory are considered simultaneously. The proposed model includes three parts: a mechanical model of skin compression, a model of transduction, and a model of modulation and perception. It is demonstrated that the pain sensation degree increases with the compression amplitude and decreases with deeper nociceptor location in the skin. With the help of the proposed model, the quantitative relationship between compression pain sensation and external mechanical stimuli is revealed, which has a significant benefit in promoting the design and mechanism research of electronic skin with pain perception function.

The design of high-aspect-ratio wings enhances the flight efficiency of UAVs but also introduces significant aeroelasticity challenges. The efficient optimization of wing structures in complex environments has become critical. To address the current challenges in balancing wing strength with lightweight structural designs, this study proposed an intelligent solution method for optimizing wing dimensions and structural layout. Driven by mechanical simulation data, the method established a mapping relationship between the structural layout and dimensions of the wing and its bending stiffness. This approach was further enhanced by the mind evolution algorithm (MEA) to optimize the solution performance of the surrogate model. The wing structure optimization model was established using the multi-objective grey wolf optimizer (MOGWO) based on the surrogate model for search and optimization. This study focused on the composite material wing of a long-endurance unmanned aerial vehicle (UAV). The established MEA-BP surrogate model demonstrated high computational efficiency, with the prediction error standard deviation (STD) of wing deflection not exceeding 0.495 mm. The optimization model required 175 s to calculate the Pareto front solutions. The optimized structure resulted in a 28.32% increase in wing equivalent stiffness, and weight only increased by 6.67% compared to the original structure. These results showcased the effectiveness of the proposed method and validated the feasibility of integrating intelligent optimization algorithms and machine learning in the field of aircraft design.

A mechanically reconfigurable flexible frequency selective surface (FSS) with dual‐band and polarization selectivity is proposed based on buckling‐guided 3D assembly strategy. The structure consists of periodically arranged symmetric double split‐ring resonators (SRRs) in a bilayer configuration. By releasing the prestrain in a stretched substrate, a deterministic 3D architecture is formed, enabling continuous and reversible structural reconfiguration under biaxial tensile strain. The deformation modulates the coupling between incident electromagnetic waves and the resonant units, resulting in polarization‐selective transmission. For transverse magnetic wave, the structure exhibits a distinct dual‐stopband response, with resonant frequencies shifting toward lower frequencies as the tensile strain increases, while high transmission is maintained for transverse electric waves across the measured frequency range. The mechanical and electromagnetic models are employed to analyze the response mechanism, and the effects of the SRR radius and gap width on resonance tuning are discussed. The proposed structure features simple fabrication, stable performance under large deformation, and precise tunability, offering promising applications in flexible tunable filters and polarization‐selective electromagnetic devices.