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Journal articles on the topic 'Inverse modeling'

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Published: 4 June 2021
Last updated: 22 June 2024

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1

Cardozo, Nestor, and Sigurd Aanonsen. "Optimized trishear inverse modeling." Journal of Structural Geology 31, no. 6 (June 2009): 546–60. http://dx.doi.org/10.1016/j.jsg.2009.03.003.

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2

Andrews, James, Hailin Jin, and Carlo Séquin. "Interactive Inverse 3D Modeling." Computer-Aided Design and Applications 9, no. 6 (January 2012): 881–900. http://dx.doi.org/10.3722/cadaps.2012.881-900.

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3

S�enz De Buruaga, Alberto, Jose C. De La Cal, and Jose M. Asua. "Modeling inverse microemulsion polymerization." Journal of Polymer Science Part A: Polymer Chemistry 37, no. 13 (July 1, 1999): 2167–78. http://dx.doi.org/10.1002/(sici)1099-0518(19990701)37:13<2167::aid-pola32>3.0.co;2-6.

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4

de Cogan, D., and A. Soulos. "INVERSE THERMAL MODELING USING TLM." Numerical Heat Transfer, Part B: Fundamentals 29, no. 1 (January 1996): 125–35. http://dx.doi.org/10.1080/10407799608914978.

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5

Chua, B. S., and A. F. Bennett. "An inverse ocean modeling system." Ocean Modelling 3, no. 3-4 (January 2001): 137–65. http://dx.doi.org/10.1016/s1463-5003(01)00006-3.

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6

Wagner, H. W., W. S. M. Werner, H. Störi, and L. M. Richardson. "Electron probe microanalysis inverse modeling." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 184, no. 3 (November 2001): 450–57. http://dx.doi.org/10.1016/s0168-583x(01)00773-x.

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7

Comanescu, Adriana, Alexandra Rotaru, Liviu Marian Ungureanu, and Florian Ion Tiberiu Petrescu. "Inverse modeling of the stewart foot." Independent Journal of Management & Production 12, no. 9 (December 21, 2021): s774—s793. http://dx.doi.org/10.14807/ijmp.v12i9.1557.

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The Stewart's leg is used today in the majority of parallel robotic systems, such as the Stewart platform, but also in many other types of mechanisms and kinematic chains, in order to operate them or to transmit motion. A special character in the study of robots is the study of inverse kinematics, with the help of which the map of the motor kinematic parameters necessary to obtain the trajectories imposed on the effector can be made. For this reason, in the proposed mechanism, we will present reverse kinematic modeling in this paper. The kinematic output parameters, ie the parameters of the foot and practically of the end effector, ie those of the point marked with T, will be determined for initiating the working algorithm with the help of logical functions, "If log(ical)", with the observation that here they play the role of input parameters; it is positioned as already specified in the inverse kinematics when the output is considered as input and the input as output. The logical functions used, as well as the entire calculation program used, were written in Math Cad.
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8

Hase, Nils, Scot M. Miller, Peter Maaß, Justus Notholt, Mathias Palm, and Thorsten Warneke. "Atmospheric inverse modeling via sparse reconstruction." Geoscientific Model Development 10, no. 10 (October 10, 2017): 3695–713. http://dx.doi.org/10.5194/gmd-10-3695-2017.

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Abstract. Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4) emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.
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9

Wu, Fuzhang, Dong-Ming Yan, Weiming Dong, Xiaopeng Zhang, and Peter Wonka. "Inverse procedural modeling of facade layouts." ACM Transactions on Graphics 33, no. 4 (July 27, 2014): 1–10. http://dx.doi.org/10.1145/2601097.2601162.

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10

Orúe‐Echevarría, Dorleta, Josep L. Pelegrí, Francisco Machín, Alonso Hernández‐Guerra, and Mikhail Emelianov. "Inverse Modeling the Brazil‐Malvinas Confluence." Journal of Geophysical Research: Oceans 124, no. 1 (January 2019): 527–54. http://dx.doi.org/10.1029/2018jc014733.

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11

Olsen, P. A., and R. A. Gopinath. "Modeling Inverse Covariance Matricesby Basis Expansion." IEEE Transactions on Speech and Audio Processing 12, no. 1 (January 2004): 37–46. http://dx.doi.org/10.1109/tsa.2003.819943.

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12

Hiskens, I. A. "Power System Modeling for Inverse Problems." IEEE Transactions on Circuits and Systems I: Regular Papers 51, no. 3 (March 2004): 539–51. http://dx.doi.org/10.1109/tcsi.2004.823654.

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13

Geurts, Bernard J. "Inverse modeling for large-eddy simulation." Physics of Fluids 9, no. 12 (December 1997): 3585–87. http://dx.doi.org/10.1063/1.869495.

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14

Chang, Michael E., Dana E. Hartley, Carlos Cardelino, and Wen-Ling Chang. "Inverse modeling of biogenic isoprene emissions." Geophysical Research Letters 23, no. 21 (October 15, 1996): 3007–10. http://dx.doi.org/10.1029/96gl02370.

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15

Katkov, Mikhail, Misha Tsodyks, and Dov Sagi. "Inverse modeling of human contrast response." Vision Research 47, no. 22 (October 2007): 2855–67. http://dx.doi.org/10.1016/j.visres.2007.06.024.

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16

Dobashi, Yoshinori, Kei Iwasaki, Makoto Okabe, Takashi Ijiri, and Hideki Todo. "Inverse appearance modeling of interwoven cloth." Visual Computer 35, no. 2 (December 4, 2017): 175–90. http://dx.doi.org/10.1007/s00371-017-1455-9.

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17

Akkaram, Srikanth, Don Beeson, Harish Agarwal, and Gene Wiggs. "Inverse modeling technology for parameter estimation." Structural and Multidisciplinary Optimization 34, no. 2 (January 9, 2007): 151–64. http://dx.doi.org/10.1007/s00158-006-0067-1.

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18

Babak, Olena. "Inverse distance interpolation for facies modeling." Stochastic Environmental Research and Risk Assessment 28, no. 6 (December 8, 2013): 1373–82. http://dx.doi.org/10.1007/s00477-013-0833-8.

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19

beim Graben, Peter, and Roland Potthast. "Inverse problems in dynamic cognitive modeling." Chaos: An Interdisciplinary Journal of Nonlinear Science 19, no. 1 (March 2009): 015103. http://dx.doi.org/10.1063/1.3097067.

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20

Barajas-Solano, D. A., B. E. Wohlberg, V. V. Vesselinov, and D. M. Tartakovsky. "Linear functional minimization for inverse modeling." Water Resources Research 51, no. 6 (June 2015): 4516–31. http://dx.doi.org/10.1002/2014wr016179.

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21

Baier, Stephan, and Volker Tresp. "Tensor Decompositions for Modeling Inverse Dynamics." IFAC-PapersOnLine 50, no. 1 (July 2017): 5630–35. http://dx.doi.org/10.1016/j.ifacol.2017.08.1110.

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22

Famoriji, O. J., and T. Shongwe. "An Effective Antenna Array Diagnosis Method via Multivalued Neural Network Inverse Modeling Approach." Advanced Electromagnetics 10, no. 3 (December 27, 2021): 58–70. http://dx.doi.org/10.7716/aem.v10i3.1784.

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Failure of element (s) in antenna arrays impair (s) symmetry and lead to unwanted distorted radiation pattern. The replacement of defective elements in aircraft antennas is a solution to the problem, but it remains a critical problem in space stations. In this paper, an antenna array diagnosis technique based on multivalued neural network (mNN) inverse modeling is proposed. Since inverse analytical input-to-output formulation is generally a challenging and important task in solving the inverse problem of array diagnosis, ANN is a compelling alternative, because it is trainable and learns from data in inverse modelling. The mNN technique proposed is an inverse modelling technique, which accommodates measurements for output model. This network takes radiation pattern samples with faults and matches it to the corresponding position or location of the faulty elements in that antenna array. In addition, we develop a new training error function, which focuses on the matching of each training sample by a value of our proposed inverse model, while the remaining values are free, and trained to match distorted radiation patterns. Thereby, mNN learns all training data by redirecting the faulty elements patterns into various values of the inverse model. Therefore, mNN is able to perform accurate array diagnosis in an automated and simpler manner.
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23

Lesshafft, Lutz, Eckart Meiburg, Ben Kneller, and Alison Marsden. "Towards inverse modeling of turbidity currents: The inverse lock-exchange problem." Computers & Geosciences 37, no. 4 (April 2011): 521–29. http://dx.doi.org/10.1016/j.cageo.2010.09.015.

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24

Morozov, Igor, Mohamed Haiba, and Wubing Deng. "Inverse attenuation filtering." GEOPHYSICS 83, no. 2 (March 1, 2018): V135—V147. http://dx.doi.org/10.1190/geo2016-0211.1.

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Inverse-[Formula: see text] filtering is an important seismic-processing operation often used to correct for attenuation and dispersion effects and increase the resolution of reflection records. However, it is important to realize that the [Formula: see text] is an apparent (phenomenological) attribute of the propagating wavefield and not guaranteed to be a material property. By recognizing the apparent character of the [Formula: see text], the attenuation-correction procedure can be significantly extended and generalized. Our approach consists of forward modeling the propagating source waveform by using multiple physical laws followed by multiple types of inverse filtering. The modeling and inverse-filtering algorithms are selectable according to the geology of the study area, data, and goals of processing, which may include reduction of attenuation effects or more general enhancements of reflectivity images. Apparent [Formula: see text] models are inherently smooth in space, which facilitates efficient use of time-variant deconvolution implemented by using overlapping tapered time windows. When using conventional [Formula: see text] models and frequency-domain deconvolution, this procedure contains all existing types of inverse-[Formula: see text] filtering. However, many more realistic forward modeling approaches can (and should) be used depending on the specific subsurface environments, such as wavefront focusing and defocusing, scattering, solid viscosity, or internal friction caused by pore-fluid flows. In general, velocity-dispersion relations cannot be inferred from the frequency-dependent [Formula: see text] and need to be considered separately. It is more precise to view frequency-dependent velocity dispersion and [Formula: see text] as concomitant and arising from a common physical mechanism of wave propagation. Time-domain deconvolution, such as an iterative method well-known in earthquake seismology, offers significant improvements in attenuation-corrected images. The approaches are illustrated on a real reflection data set by using several attenuation laws and types of deconvolution.
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25

Ko, Young-Rae, and Tae-Hyoung Kim. "Inverse Hysteresis Modeling for Piezoelectric Stack Actuators with Inverse Generalized Prandtl-Ishlinskii Model." Journal of Korean Institute of Intelligent Systems 24, no. 2 (April 25, 2014): 193–200. http://dx.doi.org/10.5391/jkiis.2014.24.2.193.

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26

Garois, Sevan, Monzer Daoud, and Francisco Chinesta. "Data-Driven Inverse Problem for Optimizing the Induction Hardening Process of C45 Spur-Gear." Metals 13, no. 5 (May 21, 2023): 997. http://dx.doi.org/10.3390/met13050997.

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Inverse problems can be challenging and interesting to study in the context of metallurgical processes. This work aims to carry out a method for inverse modeling for simultaneous double-frequency induction hardening process. In this investigation, the experimental measured hardness profiles were considered as input data, while the output data were the process parameters. For this purpose experiments were carried out on C45 steel spur-gear. The method is based on machine learning algorithms and data treatment for dealing with inverse approach issues. In addition to the inverse modeling, a forward problem-based verification completes the study. It was found that according to promising results that this method is suitable and applicable for inverse problem of hardness modeling.
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27

CHELGHOUM, Leila. "A NEW APPROACH FOR INVERSE PREISACH DISTRIBUTION FUNCTION IDENTIFICATION IN FINITE ELEMENT MODELING." Acta Electrotechnica et Informatica 17, no. 1 (March 1, 2017): 23–30. http://dx.doi.org/10.15546/aeei-2017-0004.

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28

Patel, Sharad. "Advances in Inverse Groundwater Modeling: A Comprehensive Review." International Journal of Current Microbiology and Applied Sciences 12, no. 12 (December 10, 2023): 83–100. http://dx.doi.org/10.20546/ijcmas.2023.1212.012.

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The management and sustainable use of groundwater resources are critical components in addressing global water challenges. In this context, inverse groundwater modeling has emerged as a powerful tool for characterizing subsurface properties, optimizing resource utilization, and mitigating the impacts of anthropogenic activities on aquifers. This review paper provides a comprehensive and up-to-date survey of the advancements in inverse groundwater modeling techniques, methodologies, and applications. The paper begins by presenting an overview of the fundamental principles underlying inverse modeling, elucidating the mathematical frameworks and numerical algorithms employed in estimating subsurface parameters. It explores various geophysical and hydrogeological data types commonly utilized in inverse modeling, such as hydraulic head measurements, and geophysical surveys. The integration of multiple data sources for enhancing model reliability and reducing uncertainty is also discussed. Furthermore, the review highlights recent developments in regularization techniques, sensitivity analysis, and uncertainty quantification within the context of inverse groundwater modeling. Case studies from diverse hydrogeological settings illustrate the practical applications of these methodologies in real-world scenarios, showcasing their efficacy in addressing complex groundwater management challenges, including contaminant transport, aquifer recharge, and sustainable resource exploitation. The review concludes by outlining current research gaps and future directions in the field of inverse groundwater modeling, emphasizing the need for interdisciplinary collaboration, data integration, and advanced computational approaches. This synthesis of contemporary knowledge serves as a valuable resource for researchers, practitioners, and policymakers engaged in groundwater management and environmental sustainability.
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29

Tomasek, Pavel. "Analysis of Materials Based on Inverse Modeling." Applied Mechanics and Materials 752-753 (April 2015): 369–72. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.369.

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This paper describes an interesting approach aimed at analysis of material properties. This work is based on simulated measurements of transmission coefficients of multi-layered materials. These measurements (in a waveguide) are taken as a product of a certain situation, therefore there is an inverse problem in which we try to estimate the original properties of the layers. This study employs analysis of closed-form solutions and numerical multi-parameter optimization.
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30

Finsterle, S. "Multiphase Inverse Modeling: Review and iTOUGH2 Applications." Vadose Zone Journal 3, no. 3 (August 1, 2004): 747–62. http://dx.doi.org/10.2113/3.3.747.

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31

Finsterle, Stefan. "Multiphase Inverse Modeling: Review and iTOUGH2 Applications." Vadose Zone Journal 3, no. 3 (August 2004): 747–62. http://dx.doi.org/10.2136/vzj2004.0747.

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32

Hestir, Kevin, Stephen J. Martel, Stacy Vail, Jane Long, Pete D'Onfro, and William D. Rizer. "Inverse hydrologic modeling using stochastic growth algorithms." Water Resources Research 34, no. 12 (December 1998): 3335–47. http://dx.doi.org/10.1029/98wr01549.

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33

Geman, Don. "Modeling and Inverse Problems in Image Analysis." Journal of the American Statistical Association 101, no. 474 (June 1, 2006): 847. http://dx.doi.org/10.1198/jasa.2006.s99.

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34

Laschowski, Brock, Naser Mehrabi, and John McPhee. "Inverse Dynamics Modeling of Paralympic Wheelchair Curling." Journal of Applied Biomechanics 33, no. 4 (August 2017): 294–99. http://dx.doi.org/10.1123/jab.2016-0143.

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Paralympic wheelchair curling is an adapted version of Olympic curling played by individuals with spinal cord injuries, cerebral palsy, multiple sclerosis, and lower extremity amputations. To the best of the authors’ knowledge, there has been no experimental or computational research published regarding the biomechanics of wheelchair curling. Accordingly, the objective of the present research was to quantify the angular joint kinematics and dynamics of a Paralympic wheelchair curler throughout the delivery. The angular joint kinematics of the upper extremity were experimentally measured using an inertial measurement unit system; the translational kinematics of the curling stone were additionally evaluated with optical motion capture. The experimental kinematics were mathematically optimized to satisfy the kinematic constraints of a subject-specific multibody biomechanical model. The optimized kinematics were subsequently used to compute the resultant joint moments via inverse dynamics analysis. The main biomechanical demands throughout the delivery (ie, in terms of both kinematic and dynamic variables) were about the hip and shoulder joints, followed sequentially by the elbow and wrist. The implications of these findings are discussed in relation to wheelchair curling delivery technique, musculoskeletal modeling, and forward dynamic simulations.
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35

Schulz, Volker H., Martin Siebenborn, and Kathrin Welker. "Structured Inverse Modeling in Parabolic Diffusion Problems." SIAM Journal on Control and Optimization 53, no. 6 (January 2015): 3319–38. http://dx.doi.org/10.1137/140985883.

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36

Egbert, Gary D., and Svetlana Y. Erofeeva. "Efficient Inverse Modeling of Barotropic Ocean Tides." Journal of Atmospheric and Oceanic Technology 19, no. 2 (February 2002): 183–204. http://dx.doi.org/10.1175/1520-0426(2002)019<0183:eimobo>2.0.co;2.

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37

Al-Assaad, Rayan M., and Dale M. Byrne. "Error analysis in inverse scatterometry I Modeling." Journal of the Optical Society of America A 24, no. 2 (February 1, 2007): 326. http://dx.doi.org/10.1364/josaa.24.000326.

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38

Jiang, Bo, Chao Ye, and Jun S. Liu. "Bayesian Nonparametric Tests via Sliced Inverse Modeling." Bayesian Analysis 12, no. 1 (March 2017): 89–112. http://dx.doi.org/10.1214/16-ba993.

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39

Kaipio, Jari. "Modeling of uncertainties in statistical inverse problems." Journal of Physics: Conference Series 135 (July 1, 2008): 012107. http://dx.doi.org/10.1088/1742-6596/135/1/012107.

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40

Finsterle, Stefan, and Yingqi Zhang. "Error handling strategies in multiphase inverse modeling." Computers & Geosciences 37, no. 6 (June 2011): 724–30. http://dx.doi.org/10.1016/j.cageo.2010.11.009.

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41

Lingevitch, Joseph F., Michael D. Collins, Andrew J. Fredricks, and William L. Siegmann. "Forward and inverse modeling in porous media." Journal of the Acoustical Society of America 107, no. 5 (May 2000): 2845. http://dx.doi.org/10.1121/1.429193.

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42

Hunger, Franziska, Björn Stelzner, Dimosthenis Trimis, and Christian Hasse. "Flamelet-Modeling of Inverse Rich Diffusion Flames." Flow, Turbulence and Combustion 90, no. 4 (November 21, 2012): 833–57. http://dx.doi.org/10.1007/s10494-012-9422-z.

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43

Crespo, Ricardo, and Adrienne Grêt-Regamey. "Spatially explicit inverse modeling for urban planning." Applied Geography 34 (May 2012): 47–56. http://dx.doi.org/10.1016/j.apgeog.2011.10.009.

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44

Penland, Cécile. "The Nyquist Issue in Linear Inverse Modeling." Monthly Weather Review 147, no. 4 (April 1, 2019): 1341–49. http://dx.doi.org/10.1175/mwr-d-18-0104.1.

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Abstract Linear inverse modeling (LIM) is a statistical technique based on covariance statistics that estimates the best-fit linear Markov process to a multivariate time series. An integral, often-ignored part of the technique is a test of whether or not the linear assumptions are valid. One test for linearity is the so-called tau test. While this test can be trusted when it passes, it sometimes fails when it ought to pass. In this article, we discuss one of the reasons for spurious failure, the “Nyquist issue,” which occurs when the lagged covariance matrix used in the analysis is numerically performed at a lag greater than or nearly equal to half the period of a natural mode of variability represented in the time series. As an illustration relevant to a system with many degrees of freedom, but simple enough to solve analytically, we consider a four-dimensional system consisting of two modal pairs. Within this framework, we suggest one solution that can be applied if the time series are long enough. It is hoped that awareness of this issue can prevent misinterpretation of LIM results.
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45

Kaminski, T. "Inverse Modeling of Atmospheric Carbon Dioxide Fluxes." Science 294, no. 5541 (October 12, 2001): 259a—259. http://dx.doi.org/10.1126/science.294.5541.259a.

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46

Finno, Richard J., and Michele Calvello. "Supported Excavations: Observational Method and Inverse Modeling." Journal of Geotechnical and Geoenvironmental Engineering 131, no. 7 (July 2005): 826–36. http://dx.doi.org/10.1061/(asce)1090-0241(2005)131:7(826).

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47

Menemenlis, Dimitris. "Inverse Modeling of the Ocean and Atmosphere." Eos, Transactions American Geophysical Union 83, no. 49 (2002): 580. http://dx.doi.org/10.1029/2002eo000400.

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48

Derouet-Jourdan, Alexandre, Florence Bertails-Descoubes, Gilles Daviet, and Joëlle Thollot. "Inverse dynamic hair modeling with frictional contact." ACM Transactions on Graphics 32, no. 6 (November 2013): 1–10. http://dx.doi.org/10.1145/2508363.2508398.

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49

Scarpa, Federico, Luca A. Tagliafico, and Vincenzo Bianco. "Inverse cycles modeling without refrigerant property specification." International Journal of Refrigeration 36, no. 6 (September 2013): 1716–29. http://dx.doi.org/10.1016/j.ijrefrig.2013.04.003.

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50

Jackiewicz, Jason, Nadine Nettelmann, Mark Marley, and Jonathan Fortney. "Forward and inverse modeling for jovian seismology." Icarus 220, no. 2 (August 2012): 844–54. http://dx.doi.org/10.1016/j.icarus.2012.06.028.

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