Platform development for multi-physics coupling and uncertainty analysis based on a unified framework

Qian, Guan-Hua; Li, Ren; Yang, Tao; Wang, Xu; Zhao, Pengcheng; Zhao, Yanan; Chen, Yu

doi:10.1016/j.net.2023.02.010

Cited by 3 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While uncertainty quantification has gained popularity in several deep learning domains, such as computer vision [11], human mobility [12], and medical applications [13], it is noteworthy that the modeling uncertainties in traffic forecasting has only recently started to receive attention. Recent studies, such as those presented in [14] and [15], represent some of the initial attempts in this direction. [14] uses a classical statistical method to quantify the uncertainties associated with average daily traffic volume forecasts.…”

Section: Introductionmentioning

confidence: 99%

“…Its requirement of intensive expert involvement thus limits its practicability. In [15], DeepSTUQ is employed to estimate the data and model uncertainties in traffic forecasting, based on variational inference and deep ensemble learning. This approach provides a more comprehensive understanding of the uncertainties involved, but it requires significant changes to the original model architectures, which limits its generalization ability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Modeling of Uncertainties for Traffic Forecasting

Wu,

Ye,

Zeb

et al. 2024

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Deep neural networks (DNNs) have emerged as a dominant approach for developing traffic forecasting models. These models are typically trained to minimize error on averaged test cases and produce a single-point prediction, such as a scalar value for traffic speed or travel time. However, singlepoint predictions fail to account for prediction uncertainty that is critical for many transportation management scenarios, such as determining the best-or worst-case arrival time. We present QUANTRAFFIC, a generic framework to enhance the capability of an arbitrary DNN model for uncertainty modeling. QUANTRAFFIC requires little human involvement and does not change the base DNN architecture during deployment. Instead, it automatically learns a standard quantile function during the DNN model training to produce a prediction interval for the single-point prediction. The prediction interval defines a range where the true value of the traffic prediction is likely to fall. Furthermore, QUANTRAFFIC develops an adaptive scheme that dynamically adjusts the prediction interval based on the location and prediction window of the test input. We evaluated QUANTRAFFIC by applying it to five representative DNN models for traffic forecasting across seven public datasets. We then compared QUANTRAFFIC against six uncertainty quantification methods. Compared to the baseline uncertainty modeling techniques, QUANTRAFFIC with base DNN architectures delivers consistently better and more robust performance than the existing ones on the reported datasets.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Modeling of Uncertainties for Traffic Forecasting

Wu,

Ye,

Zeb

et al. 2024

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…The recent studies presented in [11] and [12] were among the first attempt to model uncertainties in traffic forecasting. The work presented in [11] uses a classical statistical method to quantify the uncertainties associated with average daily traffic volume forecasts.…”

Section: Introductionmentioning

confidence: 99%

“…Its requirement of intensive expert involvement thus limits the practicability. In [12], a method based on variational inference and deep ensembling is employed to estimate both the data and model uncertainties in traffic forecasting. This approach provides a more comprehensive understanding of the uncertainties involved, but it requires significant changes to the original model architectures, which limits its generalization ability.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present QUANTRAFFIC, a generic framework for quantifying the prediction uncertainties of a DNNbased traffic forecasting model. Unlike prior work [11], [12], QUANTRAFFIC is designed to minimize engineering efforts and expert involvement. It can work with any DNN model without changing the underlying architecture during deployment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Design Optimization of the Cogging Torque for a PMSM Based on Manufacturing Uncertainties Analysis and Approximate Modeling

Chen

et al. 2023

Energies

View full text Add to dashboard Cite

A permanent magnet synchronous motor (PMSM) is a crucial device for power conversion in an energy system. The cogging torque of the PMSM is a crucial output characteristic, the robustness of which affects the operational reliability of the energy system. Therefore, the robust design optimization (RDO) of cogging torque has aroused widespread concern. There are several challenges in designing a robust cogging torque PMSM. In particular, some design parameters contain repetitive units, and the finite element analysis (FEA) method is time-consuming. State-of-the-art RDO methods usually treat these uncertainties from repetitive units as the same parameter, which neglects the fluctuation of the manufacturing process and cannot obtain a robust solution for the cogging torque of the motor efficiently and accurately. In order to solve this issue, an approximate modeling method based on manufacturing uncertainties analysis for RDO is proposed in this paper. First, the peak-to-peak value of cogging torque (Tcpp) is used to characterize the cogging torque, which is decoupled to an ideal component and fluctuation component produced by the center values and manufacturing tolerances of design parameters. The design of experiments (DoE) and simulation of the two components are carried out. Then, these two components are approximated separately, and the approximate model of Tcpp is obtained by adding the two components. Finally, the proposed approximate model is embedded into the RDO algorithm, and the PMSM design scheme for good Tcpp robustness is obtained. The effectiveness of the proposed method is verified through a case study of the PMSM.

show abstract