Abstract:In the fields of intelligent manufacturing, aerospace, and robotics, control systems often operate under unknown dynamics. This significantly limits the effectiveness of traditional model-based control methods. Reinforcement learning (RL), as a data-driven intelligent control approach, enables policy learning and optimization through interaction with the environment, showing great potential for solving optimal control problems in such model-unknown scenarios. This survey focuses on the issue of unknown dynamic models in continuous-time systems and first reviews the development of general reinforcement learning algorithms and their application in model-known scenarios through industrial examples and theoretical analysis methods. It also summarizes representative methods for model-unknown scenarios, such as model-based RL, off-policy integral RL, and Q-learning approaches. The survey also introduces Lyapunov-based theoretical analysis tools and important assumptions. It discusses cutting-edge topics such as RL under partial observability using large language models, safe RL, and stability and robustness enhanced RL, while highlighting the challenges faced by existing methods.

Abstract:A thermal concentrator is a thermal functional device based on transformation thermotics, effective-medium theory, and scattering-cancellation principles. By tailoring the spatial distribution of thermal conductivity or geometric configurations, it efficiently concentrates large-scale heat flux into localized regions, enabling precise control of both steady-state and transient heat transport. With advances in materials science and manufacturing technologies, research on the thermal concentrator is moving from theoretical models toward engineering implementation, and it shows application potential in microelectronic cooling, thermoelectric energy harvesting, energy heating, and thermal therapy. This article systematically reviews the physical mechanisms, structural designs, and implementation pathways of the thermal concentrator, summarizes its development and representative works, compares the applicability and performance characteristics of different theoretical frameworks and configurations, and analyzes its technical advantages and engineering feasibility in typical application scenarios. Finally, this article discusses future trends of the thermal concentrator, including extensions to complex geometries, multiscale systems, emerging energy platforms, and extreme thermal environments.

Abstract:Swarms of maritime unmanned surface vehicles (USVs), as a core technology driving the development of marine intelligence, demonstrate significant application value in military reconnaissance, environmental monitoring, maritime search and rescue, and related fields. However, the inherent characteristics of the marine environment, including highly dynamic conditions, environmental uncertainties, and communication constraints, pose formidable challenges to achieving high-performance swarm control in maritime USVs. To address these challenges, recent research advances in this field are systematically reviewed. The characteristics of the marine environment are described, the domestic and international developments in USVs are summarized, and the core control requirements and key challenges in complex marine scenarios are analyzed. Furthermore, a comprehensive survey of three representative swarm control methodologies is presented: trajectory-based guidance control, path-based guidance control, and target-based guidance control. Finally, promising research directions and future development trends in maritime USV swarm control are discussed and proposed.

Abstract:Foundation models have become a focus in radar remote sensing intelligent interpretation due to their provision of universal and generalizable solutions. Significant progress has been achieved in both theoretical and applied aspects of radar remote sensing foundation models, making it imperative to systematically summarize current research advancements. In order to further advance the research on radar remote sensing foundation models, the concept, key technologies, and evaluation methods of foundation models was expounded. Besides, current research progress and application performance are reviewed, with representative approaches and typical instances summarized. In conclusion, discussions and future directions are highlighted from four perspectives: model architecture design, interpretability research, lightweight methods, and security assessment.

Abstract:In recent years, generative artificial intelligence is progressively introduced into the field of radio spectrum cognition due to its powerful capabilities in data distribution fitting, data generation, and data completion. Compared to conventional approaches rely on physical modeling, mathematical interpolation, and discriminative artificial intelligence techniques, generative AI has significantly enhanced the accuracy of radio spectrum cognition. This paper systematically reviewed the research progress of generative artificial intelligence in radio spectrum cognition, with a focused analysis on the technical principles, application scenarios, and representative works of different generative paradigms. The challenges faced by generative AI in spectrum cognition were further discussed, including scarce training data, limited generalization in unknown scenarios, and insufficient model interpretability. In the future, by cross-modal knowledge fusion, physics-informed embedding, and the establishment of a trustworthy assessment framework, generative artificial intelligence is expected to advance radio spectrum cognition toward high precision, robust generalization, and enhanced interpretability, thereby effectively supporting the efficient utilization of spectrum resources.

Abstract:To predict the boundary-layer transition location over a flat plate across varying Mach numbers, an efficient method is developed for small-sample settings. Flow-field disturbance datasets across multiple Mach numbers were generated using the nonlinear parabolized stability equations (NPSE), with Ma = 0.01 designated as the source domain and Ma = 0.1, 0.2, 0.4, 0.8 and 1.6 as target domains. The influence of Mach number variations on transition patterns was systematically analyzed. A convolutional neural network (CNN) model was employed to map flow field patterns to transition locations, incorporating a transfer learning strategy with progressive unfreezing and layer-wise learning rates. Results demonstrate that transfer learning significantly outperforms direct training: for Ma ≤ 0.4, only 1/10 of the target domain samples are required to achieve a mean absolute error below 2.04% of the average ground-truth value; for Ma ≥ 0.8, a progressive domain adaptation strategy controls the error within 6.19%. The approach enhances transition prediction under small-sample conditions and provides a reliable technical pathway for cross-condition flow modeling.

Abstract:DA (data assimilation) is a crucial technical method for improving the accuracy of atmospheric chemical forecasts by integrating the results of atmospheric chemistry models with multi-source observational data, reducing uncertainties in model input data. Centering on DA techniques for atmospheric chemistry models, the transformation process of initial field assimilation for pollutant gases and aerosols from single state variables to multi-state variables was systematically reviewed. Meanwhile, the important progress of pollutant emission source assimilation inversion using ensemble methods and four-dimensional variational methods was focus on the improvement of emission source accuracy, optimization of spatiotemporal resolution, and enhancement of pollutant concentration prediction performance. With the explosive growth of observational data, a core challenge in the current field lied in fully leveraging high-resolution geospatial and remote sensing data for atmospheric chemical DA. The deep integration of DA with artificial intelligence algorithms represented a key research direction to break through this bottleneck and significantly enhanced the accuracy of atmospheric composition analysis and forecasting.

Abstract:The aviation and aerospace transition zones (AATZ), spanning altitudes between 50–250 km, constitutes a strategic arena for hypersonic weapon penetration and electronic warfare operations, serving as a critical battlefield that significantly impacts operational effectiveness. Artificial intelligence (AI) is profoundly empowering the region's information warfare systems, driving their evolution toward dynamic and intelligent capabilities. Key AI technologies and applications across the entire “perception-fusion-prediction-countermeasure” chain are systematically reviewed: relying on deep learning for efficient inversion of environmental parameters; utilizing intelligent fusion to construct digital twins of battlefield environments; enhancing forecast accuracy through physical information; and developing autonomous learning and game-theoretic decision-making capabilities to support precise cognition and counter-interference. AI-enabled environmental information deception and counter-deception confronts four intertwined bottlenecks: inherent uncertainty in multi-source perception, feeble interpretability of deep predictive models, poor cross-domain transferability under heterogeneous conditions, and scarcity of realistic training data. The core challenges facing AI-enabled information warfare include environmental perception uncertainty, weak model interpretability, difficulties in cross-domain transfer, and restricted data acquisition. Finally, the outlook for future development is presented, emphasizing that AI is evolving from a technical tool into a core driving force.

Abstract:Recent advances in information technology and new energy systems have introduced increasingly stringent requirements in regulating energy transport within materials. Conventional material-design paradigms are limited by inherent trade-offs among optical, thermal, and electrical transport properties, creating an urgent need for a new paradigm to fundamentally decouple and reconstruct material functionalities Recent progress on nanoporous aerogels as an enabling platform is systematically summarized, emphasizing how hierarchical structural design and cross-scale assembly of building units allow precise control of diverse energy-carrier transport. Based on this theoretical framework, advanced applications in photo–thermal–electrical energy conversion are highlighted, with particular emphasis on research progress and performance optimization pathways of aerogels for photothermal, photoelectric, thermoelectric, and integrated photo–thermal–electric systems. Finally, future research directions including AI-driven inverse design and synergistic regulation of multiple energy carriers are outlooked, providing new perspectives for the on-demand development of next-generation high-performance photo–thermal–electrical conversion materials.