To aid urban air mobility (UAM), electric vertical takeoff and landing (eVTOL) aircraft are being targeted. Conventional multidisciplinary analysis and optimization (MDAO) can be expensive, while surrogate-based optimization can struggle with challenging physical constraints. This work proposes physics-constrained generative adversarial networks (physicsGAN), to intelligently parameterize the takeoff control profiles of an eVTOL aircraft and to transform the original design space to a feasible space. Specifically, the transformed feasible space refers to a space where all designs directly satisfy all design constraints. The physicsGAN-enabled surrogate-based takeoff trajectory design framework was demonstrated on the Airbus A3 Vahana. The physicsGAN generated only feasible control profiles of power and wing angle in the feasible space with around 98.9% of designs satisfying all constraints. The proposed design framework obtained 99.6% accuracy compared with simulation-based optimal design and took only 2.2 seconds, which reduced the computational time by around 200 times. Meanwhile, data-driven GAN-enabled surrogate-based optimization took 21.9 seconds using a derivative-free optimizer, which was around an order of magnitude slower than the proposed framework. Moreover, the data-driven GAN-based optimization using gradient-based optimizers could not consistently find the optimal design during random trials and got stuck in an infeasible region, which is problematic in real practice. Therefore, the proposed physicsGAN-based design framework outperformed data-driven GAN-based design to the extent of efficiency (2.2 seconds), optimality (99.6% accurate), and feasibility (100% feasible). According to the literature review, this is the first physics-constrained generative artificial intelligence enabled by surrogate models.
Privacy Evaluation of Generative Models for Trajectory Generation
School of Electrical and Computer Engineering, National Technical University of Athens（电气与计算机工程学院，国家技术大学雅典）；Institute of Information Science and Technologies, National Research Council (CNR)（信息科学与技术研究所，国家研究委员会（CNR））；Institute of Informatics and Teletematics, National Research Council (CNR)（信息学与电信研究所，国家研究委员会（CNR））
Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics
Mila – Quebec AI Institute（魁北克AI研究所）；University of Pittsburgh（匹兹堡大学）；Peking University（北京大学）；Université de Montréal（蒙特利尔大学）；HEC Montréal（蒙特利尔HEC学院）；CIFAR AI Chair（CIFAR人工智能主席）
Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments. In the AI-enabled UAV-aided wireless networks (UAWN), multiple UAVs are employed as aerial base stations, which are capable of rapidly adapting to the dynamic environment by collecting information about the users' position and tele-traffic demands, learning from the environment and acting upon the feedback received from the users. Moreover, AI enables the interaction amongst a swarm of UAVs for cooperative optimization of the system. As a benefit of the AI …
University of Sao Paulo（圣保罗大学）；Dalhousie University（达尔豪斯大学）；Centre National de la Recherche Scientifique（法国国家科学研究中心）
Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to design an effective system that, given a patient's medical history, is able to predict probable future conditions. Previous works, mostly carried out over private datasets, have tackled the problem by using artificial neural network architectures that cannot deal with low-cardinality datasets, or by means of non-generalizable inference approaches. We introduce a Deep Learning architecture whose design results from a…
Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high power demand peaks, grid instability exacerbated by intermittent renewable generation, and global climate change amplified by rising carbon emissions. While current practices are growingly inadequate, the path to widespread adoption of artificial intelligence (AI) methods is hindered by missing aspects of trustworthiness. The CityLearn Challenge is an exemplary opportunity for researchers from multiple disciplines to investigate the potential of AI to tackle these …
Low-thrust trajectory design and in-flight control remain two of the most challenging topics for new-generation satellite operations. Most of the solutions currently implemented are based on reference trajectories and lead to sub-optimal fuel usage. Other solutions are based on simple guidance laws that need to be updated periodically, increasing the cost of operations. Whereas some optimization strategies leverage Artificial Intelligence methods, all of the approaches studied so far need either previously generated data or a strong a priori knowledge of the satellite dynamics. This study uses…
Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西大学智能组网与场景系统重点实验室（桂林电子科技大学信息与通信学院））；Research Institute of Highway Ministry of Transport（交通运输部公路科学研究院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Architecture and Transportation Engineering, Guilin University of Electronic Technology（桂林电子科技大学建筑与交通工程学院）
Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantization
Department of Computer Science, National Yang Ming Chiao Tung University（National Yang Ming Chiao Tung University计算机科学系）；Institute of Information Science, Academia Sinica（Academia Sinica信息科学研究所）；Research Center for Information Technology Innovation, Academia Sinica（Academia Sinica信息技术创新研究中心）
