Relevant bibliographies by topics / Inverse modeling

Academic literature on the topic 'Inverse modeling'

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Published: 4 June 2021
Last updated: 22 June 2024

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Journal articles on the topic "Inverse modeling"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Inverse modeling"

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Partridge, Daniel. "Inverse Modeling of Cloud – Aerosol Interactions." Doctoral thesis, Stockholms universitet, Institutionen för tillämpad miljövetenskap (ITM), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-60454.

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The role of aerosols and clouds is one of the largest sources of uncertainty in understanding climate change. The primary scientific goal of this thesis is to improve the understanding of cloud-aerosol interactions by applying inverse modeling using Markov Chain Monte Carlo (MCMC) simulation. Through a set of synthetic tests using a pseudo-adiabatic cloud parcel model, it is shown that a self adaptive MCMC algorithm can efficiently find the correct optimal values of meteorological and aerosol physiochemical parameters for a specified droplet size distribution and determine the global sensitivity of these parameters. For an updraft velocity of 0.3 m s-1, a shift towards an increase in the relative importance of chemistry compared to the accumulation mode number concentration is shown to exist somewhere between marine (~75 cm-3) and rural continental (~450 cm-3) aerosol regimes. Examination of in-situ measurements from the Marine Stratus/Stratocumulus Experiment (MASE II) shows that for air masses with higher number concentrations of accumulation mode (Dp = 60-120 nm) particles (~450 cm-3), an accurate simulation of the measured droplet size distribution requires an accurate representation of the particle chemistry. The chemistry is relatively more important than the accumulation mode particle number concentration, and similar in importance to the particle mean radius. This result is somewhat at odds with current theory that suggests chemistry can be ignored in all except for the most polluted environments. Under anthropogenic influence, we must consider particle chemistry also in marine environments that may be deemed relatively clean. The MCMC algorithm can successfully reproduce the observed marine stratocumulus droplet size distributions. However, optimising towards the broadness of the measured droplet size distribution resulted in a discrepancy between the updraft velocity, and mean radius/geometric standard deviation of the accumulation mode. This suggests that we are missing a dynamical process in the pseudo-adiabatic cloud parcel model.
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Submitted. Paper 4: Manuscript.
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Mtchedlishvili, George. "Inverse modeling of tight gas reservoirs." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola&quot, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:105-5595821.

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In terms of a considerable increase the quality of characterization of tight-gas reservoirs, the aim of the present thesis was (i) an accurate representation of specific conditions in a reservoir simulation model, induced after the hydraulic fracturing or as a result of the underbalanced drilling procedure and (ii) performing the history match on a basis of real field data to calibrate the generated model by identifying the main model parameters and to investigate the different physical mechanisms, e.g. multiphase flow phenomena, affecting the well production performance. Due to the complexity of hydrocarbon reservoirs and the simplified nature of the numerical model, the study of the inverse problems in the stochastic framework provides capabilities using diagnostic statistics to quantify a quality of calibration and reliability of parameter estimates. As shown in the present thesis the statistical criteria for model selection may help the modelers to determine an appropriate level of parameterization and one would like to have as good an approximation of structure of the system as the information permits.
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Yarlagadda, Rahul Rama Swamy. "Inverse Modeling: Theory and Engineering Examples." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1449724104.

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Qi, Te. "Inverse modeling to predict effective leakage area." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45942.

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The purpose of this research is to develop a new approach to estimate the effective leakage area using the inverse modeling process as an alternative to the blower door test. An actual office building, which is the head quarter of Energy Efficiency Hub, was used as an example case in this study. The main principle of the inverse modeling process is comparing the real monitor boiler gas consumption with the result calculated from the EnergyPlus model with a dynamic infiltration rate input to find the best estimation of the parameter of effective leakage area (ELA). This thesis considers only the feasibility of replacing the blower door test with the calibration approach, so rather than attempting an automated calibration process based on inverse modeling we deal with generating a first estimate and consider the role of model uncertainties that would make the proposed method less feasible. There are five steps of the whole process. First, we need to customize our own actual weather data (AMY) needed by the energy model (EnergyPlus model), which can help increase our quality of the result. Second, create the building energy model in EnergyPlus. Third, create a multi-zone model using CONTAM with different ELA estimation of each facade to calculate the dynamic infiltration rate of each ELA estimate. Fourth, input the dynamic infiltration rate got from the CONTAM model to EnergyPlus model and output the boiler energy consumption. Fifth, compare the boiler gas consumption from the model and the real monitor data and find the best match between the two and the corresponding ELA, which gives the best estimate from the whole inverse modeling process. From the simulation result comparison, the best estimation of the total building ELA from the inverse modeling process is the 23437cm2 at 4pa, while the result from the blower door test is 10483cm2 at 4pa. Because of the insufficient information of the building and also the uncertainty of the input parameters, the study has not led to a definite statement whether the proposed calibration of the ELA with consumption data can replace a blower door test to get an equally valid or even better ELA estimate, but it looks feasible. As this this case study is done in a deterministic context, the full feasibility test should be conducted under uncertainty. A first step towards this will talk be discussed in chapter 4.
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Shen, Jian. "Water quality modeling as an inverse problem." W&M ScholarWorks, 1996. https://scholarworks.wm.edu/etd/1539616852.

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An inverse mathematical estuarine eutrophication model has been developed. The model provides a framework to estimate unknown parameters by assimilation of the concentration data of those state variables. The inverse model developed is a laterally integrated, two-dimensional, real-time model which consists of a hydrodynamic model, an eutrophication model and an adjoint model. The hydrodynamic model provides the dynamic fields for both the eutrophication model and the adjoint model. The eutrophication model simulates eight water quality state variables which are phytoplankton, organic nitrogen, ammonium nitrogen, nitrite-nitrate nitrogen, organic phosphorus, inorganic (ortho) phosphorus, carbonaceous biochemical oxygen demand and dissolved oxygen. The adjoint model is used during the processes of parameter estimation to provide the gradients of the cost function with respect to the unknown parameters. to increase the computational efficiency and reduce computer storage space, a decoupling scheme is implemented in the inverse model, in which the kinetic processes are decoupled from the physical transport for the purpose of numerical computation. An efficient preconditioning technique is introduced in the inverse model to speed up the rate of convergence. The experiments conducted in this study provide the information of the parameter identifiability and the field data requirement for the model calibration. The model experiments with hypothetical data sets show that the parameters can be accurately estimated for short period and long period model simulations under both constant and time-varying boundary conditions. The inverse model is convergent with different initial guess parameter values and under different environments. The inverse model was successfully applied to aid calibration of the eutrophication model of the tidal Rappahannock River, Virginia. With the use of the inverse model, the eutrophication model can be calibrated efficiently and systematically. The agreement between the model predictions and observations are very satisfactory. The studies show that the inverse model is also useful in addressing the important questions of whether the estimated parameters are unique and whether the sample data are sufficient to calibrate a model. Therefore, the inverse model may also serve as a tool in helping design a field program to collect data for model calibration.
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Remy, Benjamin. "Generative modeling for weak lensing inverse problems." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASP163.

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Le lentillage gravitationnelle, qui génère un effet de déformation des images de galaxies lointaines à travers l'influence de densités de matières massives dans la ligne de visée, est très prometteur pour répondre aux questions relatives à la matière noire et à l'énergie sombre. Cet effet permet de sonder directement la distribution de matière noire dans l'Univers, qui est invisible autrement. Dans le régime où ces déformations sont faibles, il est possible de cartographier la distribution de matières projetées dans la ligne de visée, appelée carte de masse, à partir de la mesure de la déformation d'un grand nombre de galaxies. Cependant, la reconstruction de ces cartes de masse est un problème inverse qui est mal posé, à cause de données manquantes et de bruits dans le signal mesuré, et nécessite donc de l'information à priori pour être résolu. L'objectif principal de cette thèse est d'utiliser les récentes avancées sur les modèles génératifs qui permettent de modéliser des distributions complexes dans des espaces de très haute dimension. Nous proposons en particulier une nouvelle méthode pour résoudre les problème inverses de hautes dimensions et mal posés en en caractérisant la distribution a posteriori complète. En apprenant la distribution a priori à partir de de simulations cosmologiques, nous pouvons reconstruire des cartes de masses de très hautes résolution, y compris aux petites échelles, tout en en quantifiant les incertitudes associées. L'objectif principal de cette thèse est d'utiliser les récentes avancées sur les modèles génératifs qui permettent de modéliser des distributions complexes dans des espaces de très haute dimension. Nous proposons en particulier une nouvelle méthode pour résoudre les problèmes inverses de haute dimension et mal posés en en caractérisant la distribution a posteriori complète. En apprenant la distribution a priori à partir de simulations cosmologiques, nous pouvons reconstruire des cartes de masse de très haute résolution, y compris aux petites échelles, tout en en quantifiant les incertitudes associées. De plus, nous présentons une nouvelle méthode de mesure du cisaillement gravitationnel en créant un modèle décrivant les données observées au niveau des pixels. Contrairement aux méthodes standards, cette méthode ne repose pas sur la mesure d'ellipticité des galaxies et introduit donc un nouveau paradigme pour la mesure du cisaillement gravitationnel. Nous proposons en particulier un modèle hiérarchique Bayésien, avec des composantes génératives apprises et des composantes analytiques physiques. Nous montrons que cela permet de résoudre le biais de modèles dans l'estimation du cisaillement gravitationnel
Gravitational lensing, which is the effect of the distortion of distant galaxy images through the influence of massive matter densities in the line of sight, holds significant promise in addressing questions about dark matter and dark energy. It reflects the distribution of total matter of the Universe and is therefore a promising probe for cosmological models. In the case where these distortions are small, we call it the weak gravitational lensing regime and a straightforward mapping exists between the matter distribution projected in the line of sight, called mass-map, and the measured lensing effect. However, when attempting to reconstruct matter mass-maps under conditions involving missing data and high noise corruption, this linear inverse problem becomes ill-posed and may lack a meaningful solution without additional prior knowledge. The main objective of this thesis is to employ recent breakthroughs in the generative modeling literature that enable the modeling of complex distribution in high-dimensional spaces. We propose in particular a novel methodology to solve high-dimensional ill-posed inverse problems, characterizing the full posterior distribution of the problem. By learning the high dimensional prior from cosmological simulations, we demonstrate that we are able to reconstruct high-resolution 2D mass-maps alongside uncertainty quantification. Additionally, we present a new method for cosmic shear estimation based on forward modeling of the observation at the pixel level. This represents a new paradigm for weak lensing measurement as it does not rely on galaxy ellipticities anymore. In particular, we propose to build a hybrid generative and physical hierarchical Bayesian model and demonstrate that we can remove the source of model bias in the estimation of the cosmic shear
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Akhtar, Farhan Hussain. "Use of inverse modeling in air quality management." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37213.

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Inverse modeling has been used in the past to constrain atmospheric model parameters, particularly emission estimates, based upon ambient measurements. Here, inverse modeling is applied to air quality planning by calculating how emissions should change to achieve desired reduction in air pollutants. Specifically, emissions of nitrogen oxides (NOx = NO + NO2) are adjusted to achieve reductions in tropospheric ozone, a respiratory irritant, during an historic episode of elevated concentrations in urban Atlanta, GA. Understanding how emissions should change in aggregate without specifying discrete abatement options is particularly applicable to long-term and regional air pollution management. Using a cost/benefit approach, desired reductions in ozone concentrations are found for a future population in Atlanta, GA. The inverse method is applied to find NOx emission adjustments to reach this desired reduction in air pollution. An example of how emissions adjustments may aid the planning process in two neighborhoods is demonstrated using urban form indicators from a land use and transportation database. Implications of this method on establishing regional and market-based air quality management systems in light of recent legal decisions are also discussed. Both ozone and secondary particulate matter with diameters of less than 2.5μm (PM2.5) are formed in the atmosphere from common precursor species. Recent assessments of air quality management policies have stressed the need for pollutant abatement strategies addressing these mutual sources. The relative contribution of several important precursor species (NOx, sulfur dioxide, ammonia, and anthropogenic volatile organic compounds) to the formation of ozone and secondary PM2.5 in Atlanta during May 2007 - April 2008 is simulated using CMAQ/DDM-3D. This sensitivity analysis is then used to find adjustments in emissions of precursor species to achieve goal reductions for both ozone and secondary PM2.5 during a summertime episode of elevated concentrations. A discussion of the implications of these controls on air pollutant concentrations during the remaining year follows.
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Tang, Hui. "Forward and Inverse Modeling of Tsunami Sediment Transport." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/77439.

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Tsunami is one of the most dangerous natural hazards in the coastal zone worldwide. Large tsunamis are relatively infrequent. Deposits are the only concrete evidence in the geological record with which we can determine both tsunami frequency and magnitude. Numerical modeling of sediment transport during a tsunami is important interdisciplinary research to estimate the frequency and magnitude of past events and quantitative prediction of future events. The goal of this dissertation is to develop robust, accurate, and computationally efficient models for sediment transport during a tsunami. There are two different modeling approaches (forward and inverse) to investigate sediment transport. A forward model consists of tsunami source, hydrodynamics, and sediment transport model. In this dissertation, we present one state-of-the-art forward model for Sediment TRansport In Coastal Hazard Events (STRICHE), which couples with GeoClaw and is referred to as GeoClaw-STRICHE. In an inverse model, deposit characteristics, such as grain-size distribution and thickness, are inputs to the model, and flow characteristics are outputs. We also depict one trial-and-error inverse model (TSUFLIND) and one data assimilation inverse model (TSUFLIND-EnKF) in this dissertation. All three models were validated and verified against several theoretical, experimental, and field cases.
Ph. D.
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Nicot, Jean-Philippe. "Inverse modeling of subsurface environmental partitioning tracer tests /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Sadegh, Zadeh Kouroush. "Multi-scale inverse modeling in biological mass transport processes." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/4123.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Biological Resources Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Books on the topic "Inverse modeling"

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Sun, Ne-Zheng. Inverse Problems in Groundwater Modeling. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-1970-4.

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Sun, Ne-Zheng. Inverse problems in groundwater modeling. Dordrecht: Kluwer Academic, 1994.

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Sun, Ne-Zheng. Inverse Problems in Groundwater Modeling. Dordrecht: Springer Netherlands, 1999.

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Chalmond, Bernard. Modeling and Inverse Problems in Imaging Analysis. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21662-1.

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Inverse modeling of the ocean and atmosphere. Cambridge: Cambridge University Press, 2002.

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Chalmond, Bernard. Modeling and Inverse Problems in Imaging Analysis. New York, NY: Springer New York, 2003.

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Chalmond, Bernard. Modeling and inverse problems in imaging analysis. New York: Springer, 2003.

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Banks, H. Thomas. Inverse problems in the modeling of vibrations of flexible beams. Hampton, Va: ICASE, 1987.

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Zhang, Huai-Min. Application of an inverse model in the community modeling effort results. [Woods Hole, Mass: Massachusetts Institute of Technology, Woods Hole Oceanographic Institution, Joint Program in Oceanography/Applied Ocean Science and Engineering, 1995.

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On the normal inverse Gaussian distribution in modeling volatility in the financial markets. [Uppsala University]: D440istribution, Uppsala University Library, 2002.

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Book chapters on the topic "Inverse modeling"

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Fieguth, Paul. "Inverse Problems." In Statistical Image Processing and Multidimensional Modeling, 13–55. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7294-1_2.

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Vemuri, V. Rao. "Inverse Problems." In Modeling and Simulation: Theory and Practice, 89–101. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0235-7_10.

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Rappaz, Michel, Michel Bellet, and Michel Deville. "Inverse Methods." In Numerical Modeling in Materials Science and Engineering, 447–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11821-0_8.

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Bagheri, Sima. "Inverse Modeling and Validation." In SpringerBriefs in Environmental Science, 69–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46949-2_6.

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Kitanidis, Peter K. "On stochastic inverse modeling." In Subsurface Hydrology: Data Integration for Properties and Processes, 19–30. Washington, D. C.: American Geophysical Union, 2007. http://dx.doi.org/10.1029/171gm04.

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Chinesta, Francisco, and Elías Cueto. "Inverse Analysis." In PGD-Based Modeling of Materials, Structures and Processes, 171–89. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06182-5_10.

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Agami Reddy, T. "Inverse Methods." In Applied Data Analysis and Modeling for Energy Engineers and Scientists, 327–57. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-9613-8_11.

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Kunze, H., and D. La Torre. "Inverse Problems in ODEs." In Mathematical Modeling with Multidisciplinary Applications, 151–67. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118462706.ch7.

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Moura Neto, Francisco Duarte, and Antônio José da Silva Neto. "Mathematical Modeling." In An Introduction to Inverse Problems with Applications, 7–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32557-1_2.

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Han, Xu, and Jie Liu. "Computational Inverse for Modeling Parameters." In Numerical Simulation-based Design, 67–87. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-3090-1_4.

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Conference papers on the topic "Inverse modeling"

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Trunz, Elena, Sebastian Merzbach, Jonathan Klein, Thomas Schulze, Michael Weinmann, and Reinhard Klein. "Inverse Procedural Modeling of Knitwear." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00883.

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Fasanino, G., J. E. Molinard, G. de Marsily, and V. Pelce. "Inverse Modeling in Gas Reservoirs." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 1986. http://dx.doi.org/10.2118/15592-ms.

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Nammour, Rami. "Generative modeling for inverse problems." In Second International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022. http://dx.doi.org/10.1190/image2022-3745299.1.

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CURRENTI, G., R. NAPOLI, D. CARBONE, C. DEL NEGRO, and G. GANCI. "INVERSE MODELING IN GEOPHYSICAL APPLICATIONS." In Selected Contributions from the 8th SIMAI Conference. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709394_0025.

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Hannemose, Morten, Jakob Wilm, and Jeppe R. Frisvad. "Superaccurate camera calibration via inverse rendering." In Modeling Aspects in Optical Metrology VII, edited by Bernd Bodermann, Karsten Frenner, and Richard M. Silver. SPIE, 2019. http://dx.doi.org/10.1117/12.2531769.

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Li, C. James, and Hyeongceol Shin. "Tracking Bearing Spall Severity Through Inverse Modeling." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60851.

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The ability to track the severity of a spall in a bearing in operation is desirable for bearing condition monitoring and remaining life estimation. Existing technology estimates spall severity from bearing vibration r.m.s which can be affected by many factors such as operating conditions, and other nearby machine components, and therefore is of little practical use. The proposed method is a model based one and therefore is much more robust. When a spall is present, the impact between rollers and the spall produces a train of impulses which then excite the bearing and its nearby structure. The proposed method identifies a bearing dynamic model from a measured bearing vibration using a system identification method. This bearing dynamic model is then properly inverted to create a stable high fidelity inverse model that recovers the impact impulse behind a measured bearing vibration. Given the vibration of the bearing measured at a different time and therefore a possible different spall size, the inverse model estimates the impulse that excites the bearing vibration. A modeling error compensation scheme then refines the recovered impulse. Subsequently, the spall severity is estimated from the magnitude of impulse by removing the effect of bearing speed. This study also conducted experimental validations to ascertain the effectiveness of the proposed scheme in assessing spall severity.
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Chen, Jay Chung, and Lai Ah Wong. "Inverse Estimation of Estuary Flux." In Eighth International Conference on Estuarine and Coastal Modeling. Reston, VA: American Society of Civil Engineers, 2004. http://dx.doi.org/10.1061/40734(145)54.

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Tan, Yonghong. "Modeling Hysteresis Inverse in Piezoelectric Actuators Based on Inverse Hysteretic Operator." In 2007 IEEE International Conference on Networking, Sensing and Control. IEEE, 2007. http://dx.doi.org/10.1109/icnsc.2007.372764.

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Morelli, Gianfranco, and Douglas J. LaBrecque. "Robust Scheme For Ert Inverse Modeling." In 9th EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems. European Association of Geoscientists & Engineers, 1996. http://dx.doi.org/10.3997/2214-4609-pdb.205.1996_069.

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Morelli, Gianfranco, and Douglas J. LaBrecque. "Robust Scheme for ERT Inverse Modeling." In Symposium on the Application of Geophysics to Engineering and Environmental Problems 1996. Environment and Engineering Geophysical Society, 1996. http://dx.doi.org/10.4133/1.2922327.

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Reports on the topic "Inverse modeling"

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Syracuse, Ellen Marie. Inverse and Predictive Modeling. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1396096.

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GÓMEZ-HERNÁNDEZ, J. Jaime, Haiyan ZHOU, Liangping LI, and Harrie-Jan HENDRICKS FRANSSEN. Abnormal Inverse Stochastic Modeling. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0131.

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Egbert, Gary D. Inverse Modeling of Coastal Tides. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada613935.

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Egbert, Gary D. Inverse Modeling of Ocean Tides. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada627881.

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Wald, Joseph K. Solving the 'Inverse' Problem in Terrain Modeling. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada285860.

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Finsterle, S., K. Pruess, D. P. Bullivant, and M. J. O`Sullivan. Application of inverse modeling to geothermal reservoir simulation. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/463588.

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Finsterle, S. Inverse modeling of test SB4-VM2/216.7 at Wellenberg. Office of Scientific and Technical Information (OSTI), March 1994. http://dx.doi.org/10.2172/10146710.

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Ziagos, J. P., R. J. Gelinas, S. K. Doss, and R. G. Nelson. Adaptive forward-inverse modeling of reservoir fluids away from wellbores. Office of Scientific and Technical Information (OSTI), July 1999. http://dx.doi.org/10.2172/14795.

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Safta, Cosmin, Ray Bambha, and Hope Michelsen. Estimating Regional Methane Emissions Through Atmospheric Measurements and Inverse Modeling. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1569345.

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Banks, H. T. Modeling, Inverse Problems and Feedback Control for Distributed Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, November 2000. http://dx.doi.org/10.21236/ada387505.

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