Aircraft dynamics simulation using a novel physics-based learning method

Yu, Yang; Yao, Houpu; Liu, Yongming

doi:10.1016/j.ast.2019.02.021

Cited by 48 publications

(20 citation statements)

References 15 publications

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“…Flight trajectory prediction is an important tool for planning and executing a safe flight from one destination to another. The methodology behind [42] is to use a physicsbased approach to reduce the cost of simulating aircraft trajectories, which can be very computationally expensive. This type of cost increases even further when multiple aircraft trajectories need to be simulated in real time.…”

Section: Physics Based Aircraft Flight Trajectory Predictionmentioning

confidence: 99%

Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

2021

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Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft. This paper reviews the methodologies, purpose, results, and the benefits and drawbacks of each proposed method below. These various methodologies all focus on how they can leverage distinct RNN architectures such as the popular Long Short-Term Memory (LSTM) RNN or a Deep-Residual RNN. This paper also examines which frameworks work best in certain situations, and the advantages and disadvantages of each proposed model.

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Section: Physics Based Aircraft Flight Trajectory Predictionmentioning

confidence: 99%

Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

2021

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“…Among the numerous applications that machine learning offers to exemplary and current GNC issues (see [23,[31][32][33][34]), its potential to precisely estimate the gravity vector from sensor information is one of the main unexplored settings. The utilization of neural networks (NN) to understand the evolution of nonlinear equations has been demonstrated before [35], regardless of uncertainty.…”

Section: Neural Network Based Gravity Vector Estimationmentioning

confidence: 99%

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

2020

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The Guidance, Navigation and Control (GNC) of air and space vehicles has been one of the spearheads of research in the aerospace field in recent times. Using Global Navigation Satellite Systems (GNSS) and inertial navigation systems, accuracy may be detached from range. However, these sensor-based GNC systems may cause significant errors in determining attitude and position. These effects can be ameliorated using additional sensors, independent of cumulative errors. The quadrant photodetector semiactive laser is a good candidate for such a purpose. However, GNC systems’ development and construction costs are high. Reducing costs, while maintaining safety and accuracy standards, is key for development in aerospace engineering. Advanced algorithms for getting such standards while eliminating sensors are cornerstone. The development and application of machine learning techniques to GNC poses an innovative path for reducing complexity and costs. Here, a new nonlinear hybridization algorithm, which is based on neural networks, to estimate the gravity vector is presented. Using a neural network means that once it is trained, the physical-mathematical foundations of flight are not relevant; it is the network that returns dynamics to be fed to the GNC algorithm. The gravity vector, which can be accurately predicted, is used to determine vehicle attitude without calling for gyroscopes. Nonlinear simulations based on real flight dynamics are used to train the neural networks. Then, the approach is tested and simulated together with a GNC system. Monte Carlo analysis is conducted to determine performance when uncertainty arises. Simulation results prove that the performance of the presented approach is robust and precise in a six-degree-of-freedom simulation environment.

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“…L. Yang et al (2020) proposed a physics-informed Wasserstein GAN to solve forward, inverse, and mixed problems related to stochastic PDEs. In addition to solving PDEs, physicsinformed ML has also been adopted for other engineering applications such as dynamic response prediction (Yu et al, 2019;R. Zhang et al, 2020), climate modeling (Daw et al, 2020;Jia et al, 2019), SHM (Dourado & Viana, 2020;Z.…”

Section: Introductionmentioning

confidence: 99%

“…(2020) proposed a physics‐informed Wasserstein GAN to solve forward, inverse, and mixed problems related to stochastic PDEs. In addition to solving PDEs, physics‐informed ML has also been adopted for other engineering applications such as dynamic response prediction (Yu et al., 2019; R. Zhang et al., 2020), climate modeling (Daw et al., 2020; Jia et al., 2019), SHM (Dourado & Viana, 2020; Z. Zhang & Sun, 2020), and so on. In this study, we seek to combine the powerful distribution learning ability of the data‐driven GAN and the underlying physics of BWIM methodology to propose a physics‐constrained GAN for probabilistic vehicle weight estimation.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic vehicle weight estimation using physics‐constrained generative adversarial network

Cai

Liu

2021

Computer aided Civil Eng

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Traffic information plays an important role in the design and management of civil transportation infrastructure. Bridge weigh-in-motion (BWIM) provides an effective tool for traffic information gathering by estimating vehicle parameters including its weight through bridge responses. Most existing BWIM algorithms rarely consider the epistemic uncertainty of vehicle weight in terms of the probabilistic distribution of estimated axle weights (AWs) of the vehicle. This paper proposes a novel methodology for probabilistic vehicle weight estimation using a physics-constrained generative adversarial network (GAN). Generative models are introduced to describe the probabilistic distributions of estimated AWs and bridge responses. Physics constraints on the generative models are formulated and enforced by minimizing a physics-based loss function. The generative models are then learned by training a physics-constrained GAN using the observed bridge responses. Numerical study and field testing are conducted to demonstrate the proposed method using representative highway bridges and vehicles.The results show that the proposed method can successfully capture the uncertainty in the vehicle weight estimation and provide the probabilistic distributions of the estimated AWs for different vehicle types and loading conditions considered, which can enhance the application of BWIM for relevant tasks such as traffic data collection and truck overloading enforcement. Based on the results obtained from the numerical study and field testing, the maximum coefficient of variation obtained for the AWs and gross vehicle weight of the presented cases are 0.55 and 0.11, respectively.

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Aircraft dynamics simulation using a novel physics-based learning method

Cited by 48 publications

References 15 publications

Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

Probabilistic vehicle weight estimation using physics‐constrained generative adversarial network

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