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Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs Article

Junling Fang, Bin Gong, Jef Caers

Engineering 2022, Volume 18, Issue 11,   Pages 116-128 doi: 10.1016/j.eng.2022.04.015

Abstract: Bayesian theorem provides a framework to quantify the uncertainty in geological modeling and flow simulationThe application of Bayesian methods to fractured reservoirs has mostly been limited to synthetic casesIn field applications, however, one of the main problems is that the Bayesian prior is falsified, becauseWe then employ an approximate Bayesian computation (ABC) method combined with a tree-based surrogate

Keywords： Bayesian evidential learning     Falsification     Fractured reservoir     Random forest     Approximate Bayesian computation    

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AGCD: a robust periodicity analysis method based on approximate greatest common divisor

Juan YU,Pei-zhong LU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6,   Pages 466-473 doi: 10.1631/FITEE.1400345

Abstract: To solve the problem, a novel method based on the approximate greatest common divisor (AGCD) is proposed

Keywords： Periodicity analysis     Period detection     Sparsity     Noise     Approximate greatest common divisor (AGCD)    

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Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 1,   Pages 80-98 doi: 10.1007/s11709-021-0682-3

Abstract: a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian

Keywords： Bayesian belief network     seismically induced soil liquefaction     interpretive structural modeling     lateral    

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A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

Frontiers of Engineering Management 2017, Volume 4, Issue 4,   Pages 483-489 doi: 10.15302/J-FEM-2017023

Abstract: Bayesian inference was employed to calibrate the model by using prior data obtained from a previous controlled

Keywords： pedestrian flow model     bi-directional interactions     empirical studies     Bayesian inference    

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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 277-291 doi: 10.1007/s11708-021-0731-6

Abstract: values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian

Keywords： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate    

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Current Status and Future Development of Quantum Computation

Li Xiaowei, Fu Xiang, Yan Fei, Zhong Youpeng, Lu Chaoyang, Zhang Junhua, He Yu, Yu Shi, Lu Dawei, Xin Tao, Chen Jilei, Lin Benchuan, Zhang Zhensheng, Liu Song, Chen Yuanzhen, Yu Dapeng

Strategic Study of CAE 2022, Volume 24, Issue 4,   Pages 133-144 doi: 10.15302/J-SSCAE-2022.04.016

Abstract: computation, silicon-based quantum computation, as well as other systems.quantum computation, distributed superconducting quantum computation, photonic quantum computation,computation, nitrogen-vacancy (NV) centers in diamond, NMR quantum computation, quantum computationIn 2016, Farhi et al. proposed a quantum approximate optimization algorithm (QAOA) based on the VQE algorithmstyle="text-align: justify;">As a shallow variational algorithm, the QAOA algorithm is used for the approximate

Keywords： quantum computation     quantum algorithm     control system of quantum computation     quantum software     superconductingquantum computation     distributed quantum computation     trapped-ion quantum computation     silicon-basedquantum computation     photonic quantum computation     neutral atom quantum computation     nitrogen-vacancycolor center in diamond     nuclear magnetic resonance quantum computation     quantum computation with spinwave     topological quantum computation    

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Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 7, doi: 10.1007/s11783-023-1685-1

Abstract:

● A hydrodynamic-Bayesian inference model was developed for water

Keywords： Identification of pollution sources     Water quality restoration     Bayesian inference     Hydrodynamic model    

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HIGH-PERFORMANCE COMPUTATION AND ARTIFICIAL INTELLIGENCE IN PESTICIDE DISCOVERY: STATUS AND OUTLOOK

Frontiers of Agricultural Science and Engineering 2022, Volume 9, Issue 1,   Pages 150-154 doi: 10.15302/J-FASE-2021419

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Spectral element modeling based structure piezoelectric impedance computation and damage identification

Zhigang GUO, Zhi SUN

Frontiers of Structural and Civil Engineering 2011, Volume 5, Issue 4,   Pages 458-464 doi: 10.1007/s11709-011-0133-7

Abstract: This paper presents a numerical simulation study on electromechanical impedance technique for structural damage identification. The basic principle of impedance based damage detection is structural impedance will vary with the occurrence and development of structural damage, which can be measured from electromechanical admittance curves acquired from PZT patches. Therefore, structure damage can be identified from the electromechanical admittance measurements. In this study, a model based method that can identify both location and severity of structural damage through the minimization of the deviations between structural impedance curves and numerically computed response is developed. The numerical model is set up using the spectral element method, which is promised to be of high numerical efficiency and computational accuracy in the high frequency range. An optimization procedure is then formulated to estimate the property change of structural elements from the electric admittance measurement of PZT patches. A case study on a pin-pin bar is conducted to investigate the feasibility of the proposed method. The results show that the presented method can accurately identify bar damage location and severity even when the measurements are polluted by 5% noise.

Keywords： PZT     piezoelectric impedance     optimization     spectral element     damage identification    

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An Approximate Analytical Method for a Slot Ring Radome Article

Kang Luo, Jin Meng, Jiangfeng Han, Danni Zhu

Engineering 2023, Volume 30, Issue 11,   Pages 75-82 doi: 10.1016/j.eng.2023.07.015

Abstract: out-of-band microwave wireless power-transmission characteristic analysis of a slot ring radome based on an approximateThe main contribution of this paper is that, in the approximate analysis of the ring radome, a unified

Keywords： Approximate analytical     Ring     Radome     Kirchhoff-type circuit    

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Iteration framework for solving mixed lubrication computation problems

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 3,   Pages 635-648 doi: 10.1007/s11465-021-0632-8

Abstract: The general discrete scheme of time-varying Reynolds equation loses the information of the previous step, which makes it unreasonable. A discretization formula of the Reynolds equation, which is based on the Crank–Nicolson method, is proposed considering the physical message of the previous step. Gauss–Seidel relaxation and distribution relaxation are adopted for the linear operators of pressure during the numerical solution procedure. In addition to the convergent criteria of pressure distribution and load, an estimation framework is developed to investigate the relative error of the most important term in the Reynolds equation. Smooth surface with full contacts and mixed elastohydrodynamic lubrication is tested for validation. The asperity contact and sinusoidal wavy surface are examined by the proposed discrete scheme. Results show the precipitous decline in the boundary of the contact area. The relative error suggests that the pressure distribution is reliable and reflects the accuracy and effectiveness of the developed method.

Keywords： mixed lubrication     discretization formula     relative error     Reynolds equation     asperity    

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Multi-dimensional optimization for approximate near-threshold computing

Jing Wang, Wei-wei Liang, Yue-hua Niu, Lan Gao, Wei-gong Zhang,jwang@cnu.edu.cn,zwg771@cnu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 10,   Pages 1413-1534 doi: 10.1631/FITEE.2000089

Abstract: We use a dynamic programming algorithm to determine the proper voltage and approximate level based on
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Scientific computation of big data in real-world clinical research

Guozheng Li,Xuewen Zuo,Baoyan Liu

Frontiers of Medicine 2014, Volume 8, Issue 3,   Pages 310-315 doi: 10.1007/s11684-014-0358-7

Abstract: This study describes the origin, concept, connotation, and value of studies regarding the scientific computation

Keywords： big data     real world     clinical research     Chinese medicine     medical computing    

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1285-1298 doi: 10.1007/s11709-020-0691-7

Abstract: Computational homogenization methods for nonlinear material and implementation of offline multiscale computation

Keywords： multiscale method     constitutive model     feedforward neural network     recurrent neural network    

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A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 8,   Pages 617-633 doi: 10.1631/FITEE.1400263

Abstract: A local discriminant regularized soft -means (LDRSKM) method with Bayesian inference is proposed forTwo Bayesian inference based global fault detection indicators are then developed using the local monitoringBased on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode

Keywords： monitoring     Local discriminant regularized soft k-means clustering     Kernel support vector data description     Bayesian    

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Title Author Date Type Operation

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Junling Fang, Bin Gong, Jef Caers

Journal Article

AGCD: a robust periodicity analysis method based on approximate greatest common divisor

Juan YU,Pei-zhong LU

Journal Article

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

Journal Article

A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

Journal Article

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Journal Article

Current Status and Future Development of Quantum Computation

Li Xiaowei, Fu Xiang, Yan Fei, Zhong Youpeng, Lu Chaoyang, Zhang Junhua, He Yu, Yu Shi, Lu Dawei, Xin Tao, Chen Jilei, Lin Benchuan, Zhang Zhensheng, Liu Song, Chen Yuanzhen, Yu Dapeng

Journal Article

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov

Journal Article

HIGH-PERFORMANCE COMPUTATION AND ARTIFICIAL INTELLIGENCE IN PESTICIDE DISCOVERY: STATUS AND OUTLOOK

Journal Article

Spectral element modeling based structure piezoelectric impedance computation and damage identification

Zhigang GUO, Zhi SUN

Journal Article

An Approximate Analytical Method for a Slot Ring Radome

Kang Luo, Jin Meng, Jiangfeng Han, Danni Zhu

Journal Article

Iteration framework for solving mixed lubrication computation problems

Journal Article

Multi-dimensional optimization for approximate near-threshold computing

Jing Wang, Wei-wei Liang, Yue-hua Niu, Lan Gao, Wei-gong Zhang,jwang@cnu.edu.cn,zwg771@cnu.edu.cn

Journal Article

Scientific computation of big data in real-world clinical research

Guozheng Li,Xuewen Zuo,Baoyan Liu

Journal Article

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Journal Article

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

Journal Article