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Journal articles on the topic 'Forward modeling'

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Published: 4 June 2021
Last updated: 13 July 2024

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1

Acar, Z. Akalin, and S. Makeig. "Neuroelectromagnetic Forward Modeling Toolbox." NeuroImage 47 (July 2009): S74. http://dx.doi.org/10.1016/s1053-8119(09)70473-2.

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2

Karna, Nishu, Antonia Savcheva, Kévin Dalmasse, Sarah Gibson, Svetlin Tassev, Giuliana de Toma, and Edward E. DeLuca. "Forward Modeling of a Pseudostreamer." Astrophysical Journal 883, no. 1 (September 23, 2019): 74. http://dx.doi.org/10.3847/1538-4357/ab3c50.

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3

Aiouaz, T., H. Peter, and R. Keppens. "Forward modeling of coronal funnels." Astronomy & Astrophysics 442, no. 3 (October 14, 2005): L35—L38. http://dx.doi.org/10.1051/0004-6361:200500183.

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4

Acar, Zeynep Akalin, and Scott Makeig. "Neuroelectromagnetic Forward Head Modeling Toolbox." Journal of Neuroscience Methods 190, no. 2 (July 2010): 258–70. http://dx.doi.org/10.1016/j.jneumeth.2010.04.031.

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5

CHEN, Lin, Hai-Bin SONG, Chong-Zhi DONG, Jiong ZHANG, and Chang-Yu ZHAO. "2D Strain Rate Forward Modeling." Chinese Journal of Geophysics 51, no. 6 (November 2008): 1194–202. http://dx.doi.org/10.1002/cjg2.1316.

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6

BERMIN, HANS-PETER, and GARETH WILLIAMS. "ON CASH SETTLED IRR-SWAPTIONS AND MARKOV FUNCTIONAL MODELING." International Journal of Theoretical and Applied Finance 20, no. 02 (March 2017): 1750009. http://dx.doi.org/10.1142/s0219024917500091.

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In this paper, we show how to consistently price cash settled Internal Rate of Return (IRR)-swaptions and derivatives on these contracts. There are several results worth highlighting. First, if we know at what fixed coupon an IRR-swap values to par, we can compute the price of any IRR-swaption in a way consistent with absence of arbitrage. We show that this fixed coupon, denoted the IRR-forward, carries an additional convexity adjustment. The size of the adjustment depends mainly on the shape of the volatility surface but also on the skew of the forward. The largest convexity adjustments are seen for IRR-forwards referencing long tenors and long expiries. Second, we show that any Markov functional technique, relating a given term-structure model to the market observed IRR-swaptions, should be carried out with respect to the corresponding forward measure. The modification of the forward swap rate is further shown to consistently value the fixed and the floating leg of the underlying IRR-swap correctly.
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7

He, Qiang. "Modeling and Control of Forward Converter." Applied Mechanics and Materials 130-134 (October 2011): 1986–89. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.1986.

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The average model of the forward converter is build with the state-space average method. The open-loop transfer function model is deduced in detail according to the average model of the forward converter, the controller is designed based frequency domain by the type III compensation network. And the stability of the control system has been improved. The modeling and simulation of system was implemented based Matlab. The results of simulation confirm that the controller is capable of reduced steady state error and improve controller's reliability during power supply disturbance and load disturbance．
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8

Bruderer, Claudio, Andrina Nicola, Adam Amara, Alexandre Refregier, Jörg Herbel, and Tomasz Kacprzak. "Cosmic shear calibration with forward modeling." Journal of Cosmology and Astroparticle Physics 2018, no. 08 (August 8, 2018): 007. http://dx.doi.org/10.1088/1475-7516/2018/08/007.

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9

Endignoux, L., I. Moretti, and F. Roure. "Forward modeling of the Southern Apennines." Tectonics 8, no. 5 (October 1989): 1095–104. http://dx.doi.org/10.1029/tc008i005p01095.

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10

Maxwell, Reed M., Alexis Navarre-Sitchler, and Matt Tonkin. "Forward: Modeling for Sustainability and Adaptation." Groundwater 56, no. 4 (June 13, 2018): 515–16. http://dx.doi.org/10.1111/gwat.12795.

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11

Woodbury, Allan D., and T. J. Ulrych. "Minimum relative entropy: Forward probabilistic modeling." Water Resources Research 29, no. 8 (August 1993): 2847–60. http://dx.doi.org/10.1029/93wr00923.

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12

Grechka, Vladimir. "Effective media: A forward modeling view." GEOPHYSICS 68, no. 6 (November 2003): 2055–62. http://dx.doi.org/10.1190/1.1635059.

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The existing effective media theories, such as Backus averaging, can be only used in media that possess certain characteristics regarding to concentration, shape, or geometry of their heterogeneities. These limitations originate from necessity of having analytical description of complex stress and strain fields that normally arise in microheterogeneous solids. The need for explicit solutions can be eliminated by computing the stresses and strains numerically. As a result, effective media can be in principle constructed for solids of arbitrary complexity. This simple idea is tested on two 2D models, where conventional analytical effective media theories are likely to break down. The first model is an isotropic layered solid with cracks that intersect the layer interfaces, the second is a layered medium containing random inclusions. In both cases, some differences are observed between the effective stiffness coefficients obtained numerically and those derived using the existing effective media theories. For instance, it is demonstrated that Backus averaging (improperly) applied to horizontal isotropic layers with random inclusions leads to biases in the calculated vertical velocities. Overall, the proposed technique enables us to establish the limits of applicability of conventional effective media theories. It also makes it possible to obtain quantitative estimates of the errors incurred because of violating certain assumptions of a given theory.
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13

Hart, Bruce, and Marc-André Chen. "Understanding seismic attributes through forward modeling." Leading Edge 23, no. 9 (September 2004): 834–41. http://dx.doi.org/10.1190/1.1803492.

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14

Bauer, Daniel, Fred Espen Benth, and Rüdiger Kiesel. "Modeling the Forward Surface of Mortality." SIAM Journal on Financial Mathematics 3, no. 1 (January 2012): 639–66. http://dx.doi.org/10.1137/100818261.

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15

Nordlund, Ulf. "FUZZIM: forward stratigraphic modeling made simple." Computers & Geosciences 25, no. 4 (May 1999): 449–56. http://dx.doi.org/10.1016/s0098-3004(98)00151-4.

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16

Povh, M., and S. E. Fleten. "Modeling Long-Term Electricity Forward Prices." IEEE Transactions on Power Systems 24, no. 4 (November 2009): 1649–56. http://dx.doi.org/10.1109/tpwrs.2009.2030285.

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17

Ford, Oliver, J. Svensson, A. Boboc, and D. C. McDonald. "Forward modeling of JET polarimetry diagnostic." Review of Scientific Instruments 79, no. 10 (October 2008): 10F324. http://dx.doi.org/10.1063/1.2956880.

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18

Fanchi, John R. "Integrating forward modeling into reservoir simulation." Journal of Petroleum Science and Engineering 32, no. 1 (December 2001): 11–21. http://dx.doi.org/10.1016/s0920-4105(01)00144-9.

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19

Radstake, F., W. Geirnaert, T. W. Kleinendorsf, and J. C. Terhell. "Applications of Forward Modeling Resistivity Profiles." Ground Water 29, no. 1 (January 1991): 13–17. http://dx.doi.org/10.1111/j.1745-6584.1991.tb00490.x.

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20

Ouraini, Fadila, Ramon Carbonell, David Marti Linares, Puy Ayarza, Kamal Gueraoui, and Mimoune Harnafi. "Forward modeling of SIMA seismic line." Contemporary Engineering Sciences 8 (2015): 729–36. http://dx.doi.org/10.12988/ces.2015.53196.

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21

Radstake, F., W. Geirnaert, T. W. Kleinendorst, and J. C. Terhell. "Application of forward modeling resistivity profiles." International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 28, no. 6 (November 1991): A360. http://dx.doi.org/10.1016/0148-9062(91)91367-z.

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22

Bai, Peng, Giulio Vignoli, and Thomas Mejer Hansen. "1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error." Remote Sensing 13, no. 19 (September 28, 2021): 3881. http://dx.doi.org/10.3390/rs13193881.

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Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.
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23

Smith, Jane McKee, and Patrick J. Lynett. "Foreword: Proceedings of the 32nd International Conference." Coastal Engineering Proceedings 1, no. 32 (February 6, 2011): 1. http://dx.doi.org/10.9753/icce.v32.forward.1.

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This Proceedings contains 360 papers and 15 posters presented at the 32nd International Conference on Coastal Engineering, which was held in Shanghai, China, 30 June to 5 July 2010. The Proceedings is divided into seven parts: Keynote; Waves; Swash, Nearshore Currents, and Long Waves; Sediment Transport and Morphology; Coastal Structures; Coastal Management, Environment, and Risk, and Posters. The individual papers cover a broad range of topics including theory, numerical and physical modeling, field measurements, case studies, design, and management. These papers provide engineers, scientists, and planners state-of-the-art information on coastal engineering and coastal processes.
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24

Yatini, Yatini, Djoko Santoso, Agus Laesanpura, and Budi Sulistijo. "Physical Modeling on Time Domain Induced Polarization (TDIP) Response of Metal Mineral Content." INDONESIAN JOURNAL OF APPLIED PHYSICS 8, no. 2 (November 23, 2018): 57. http://dx.doi.org/10.13057/ijap.v8i1.20648.

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The Induced Polarization (IP) methods is an extension of resistivity method by adding ability of the ground in storing electrical charge. One of the measurement technique is done in time domain, hereinafter referred to as Time Domain Induced Polarization (TDIP). TDIP responses measured on the surface are affected by the physical properties of the subsurface. Research in TDIP response modeling studies is performed to obtain a quantitative relationship between response to metallic mineral content at subsurface. The relationship can be obtained by forward and physical modelling. The forward modeling produces a curve that connects TDIP response to the subsurface parameters and an array. The laboratory-scale physical model is performed on the sand-box size (200x100x70) cm3 by varying iron-ore content in a sphere target. TDIP response measurements on physical models is done using Dipole-dipole and Wenner configuration. The relationship between the TDIP response and metal mineral content is obtained by comparing the results of measurements on physical modeling and forward modelling. There is good appropriatement between the theoretical curves and measuring results of the physical modelling. The greater of iron-ore content on the target, increasing in the TDIP response.
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25

Carlin, E. S., and A. Asensio Ramos. "CHROMOSPHERIC DIAGNOSIS WITH Ca II LINES: FORWARD MODELING IN FORWARD SCATTERING. I." Astrophysical Journal 801, no. 1 (February 26, 2015): 16. http://dx.doi.org/10.1088/0004-637x/801/1/16.

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26

Vasin, A. A., and A. G. Gusev. "Mathematical modeling of the forward contract market." Moscow University Computational Mathematics and Cybernetics 32, no. 3 (September 2008): 140–46. http://dx.doi.org/10.3103/s0278641908030047.

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27

Li, A., and S. L. Butler. "Forward modeling of magnetotellurics using Comsol Multiphysics." Applied Computing and Geosciences 12 (December 2021): 100073. http://dx.doi.org/10.1016/j.acags.2021.100073.

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28

Collins, Michael D., Joseph F. Lingevitch, and William L. Siegmann. "Forward modeling techniques for range‐dependent problems." Journal of the Acoustical Society of America 102, no. 5 (November 1997): 3142. http://dx.doi.org/10.1121/1.420678.

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29

Louboutin, Mathias, Philipp Witte, Michael Lange, Navjot Kukreja, Fabio Luporini, Gerard Gorman, and Felix J. Herrmann. "Full-waveform inversion, Part 1: Forward modeling." Leading Edge 36, no. 12 (December 2017): 1033–36. http://dx.doi.org/10.1190/tle36121033.1.

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Since its reintroduction by Pratt (1999) , full-waveform inversion (FWI) has gained a lot of attention in geophysical exploration because of its ability to build high-resolution velocity models more or less automatically in areas of complex geology. While there is an extensive and growing literature on the topic, publications focus mostly on technical aspects, making this topic inaccessible for a broader audience due to the lack of simple introductory resources for newcomers to computational geophysics. We will accomplish this by providing a hands-on walkthrough of FWI using Devito ( Lange et al., 2016 ), a system based on domain-specific languages that automatically generates code for time-domain finite differences.
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30

Xiong, Dewen, and Michael Kohlmann. "Modeling the Forward CDS Spreads with Jumps." Stochastic Analysis and Applications 30, no. 3 (May 2012): 375–402. http://dx.doi.org/10.1080/07362994.2012.668435.

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31

Connors, Christopher D., Amanda N. Hughes, and Stephen M. Ball. "Forward kinematic modeling of fault-bend folding." Journal of Structural Geology 143 (February 2021): 104252. http://dx.doi.org/10.1016/j.jsg.2020.104252.

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32

Matthews, R. K., and Cliff Frohlich. "Forward modeling of bank-margin carbonate diagenesis." Geology 15, no. 7 (1987): 673. http://dx.doi.org/10.1130/0091-7613(1987)15<673:fmobcd>2.0.co;2.

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33

Erickson, Kelvin T., and Robert E. Otto. "Development of a multivariable forward modeling controller." Industrial & Engineering Chemistry Research 30, no. 3 (March 1991): 482–90. http://dx.doi.org/10.1021/ie00051a008.

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34

Erickson, K. T., and R. E. Otto. "Development of a multivariable forward modeling controller." IFAC Proceedings Volumes 21, no. 4 (June 1988): 69–74. http://dx.doi.org/10.1016/b978-0-08-035735-5.50014-0.

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35

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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36

KELAMIS, P. G., and E. KJARTANSSON. "FORWARD MODELING IN THE FREQUENCY-SPACE DOMAIN*." Geophysical Prospecting 33, no. 2 (April 1985): 252–62. http://dx.doi.org/10.1111/j.1365-2478.1985.tb00433.x.

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37

Hui, Cho Hoi. "Modeling Forward Credit Risk — An Option Approach." Journal of Fixed Income 9, no. 2 (September 30, 1999): 54–61. http://dx.doi.org/10.3905/jfi.1999.319260.

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38

Dinklage, A., R. Reimer, R. Wolf, and M. Reich. "Forward Modeling of Motional Stark Effect Spectra." Fusion Science and Technology 59, no. 2 (February 2011): 406–17. http://dx.doi.org/10.13182/fst11-a11655.

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39

Van Grootel, V., S. Charpinet, G. Fontaine, P. Brassard, E. M. Green, P. Chayer, and S. K. Randall. "Testing the forward modeling approach in asteroseismology." Astronomy & Astrophysics 488, no. 2 (July 9, 2008): 685–96. http://dx.doi.org/10.1051/0004-6361:200809867.

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40

Charpinet, S., V. Van Grootel, D. Reese, G. Fontaine, E. M. Green, P. Brassard, and P. Chayer. "Testing the forward modeling approach in asteroseismology." Astronomy & Astrophysics 489, no. 1 (July 23, 2008): 377–94. http://dx.doi.org/10.1051/0004-6361:200809907.

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41

Mercy Shalinie, S. "Modeling parallel feed-forward based compression network." International Journal of Parallel, Emergent and Distributed Systems 21, no. 4 (August 2006): 227–37. http://dx.doi.org/10.1080/17445760600567859.

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42

Carpenter, Chris. "Stratigraphic Forward Modeling Assists Carbonate-Reservoir Characterization." Journal of Petroleum Technology 74, no. 09 (September 1, 2022): 60–63. http://dx.doi.org/10.2118/0922-0060-jpt.

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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 202775, “Application of Stratigraphic Forward Modeling to Carbonate-Reservoir Characterization: A New Paradigm From the Albion Research and Development Project,” by Jean Borgomano, Aix-Marseille University, and Gérard Massonnat and Cyprien Lanteaume, TotalEnergies, et al. The paper has not been peer reviewed. _ Improving carbonate-reservoir prediction, field development, and production forecasts, especially in zones lacking data, requires novel reservoir-modeling approaches, including process-based methods. Classical geostatistic modeling methods alone cannot match this challenge, particularly if subtle stratigraphic architectures or sedimentary and diagenetic geometries not directly identified as properties with well data control the reservoir heterogeneity. Stratigraphic forward-modeling approaches can provide pertinent information to carbonate-reservoir characterization. The complete paper describes a modeling package tested and calibrated with high-resolution stratigraphic outcrop models. It allows valid prediction of carbonate facies associations mimicking the spatial distribution mapped along the Urgonian platform transects. Background Classical carbonate-reservoir characterization protocols rely mainly on 3D geostatistical models based on well data, allowing the realization of 3D numerical grids of reservoir properties. These geostatistic property models are supported by deterministic geological interpretations such as stratigraphic well correlations that are commonly based on sequenced stratigraphic concepts and carbonate sedimentological interpretations. The stratigraphic framework obtained from these deterministic interpretations has a critical effect on further static and dynamic reservoir models because it constrains the spatial stationarity of the geostatistic property simulations or imposes discrete flow units or barriers. These deterministic carbonate sequence stratigraphic and associated sedimentological interpretations, however, introduce significant biases, uncertainties, and imprecisions in reservoir models and furthermore are not validated by process-based modeling approaches as one should expect from any scientific protocol. This lack of validation represents a fundamental scientific gap in classical reservoir-characterization work flows that is generally avoided in other scientific domains such as physics by iterations combining experimentation and process-based models to verify deterministic interpretations and hypothesis. The paradox is that this virtuous scientific method is applied at the ultimate stage of the reservoir flow modeling with the classical “flow history matching,” implying the following strong hypothesis (Fig. 1a): If the dynamic model obtained from the upscaled static model matches the dynamic history and the flow records of the studied field and carbonate reservoir, then the geological model, including the deterministic stratigraphic and sedimentary interpretations, is validated. Reservoir flow and dynamic behavior certainly are controlled by initial geological conditions, but those are not dependent on flow processes. According to fundamental scientific principles, geological interpretations and deterministic models must be validated by geological process-based models. To fill this scientific gap in the presented carbonate-reservoir characterization approach, the authors introduce process-based stratigraphical and sedimentological models that are calibrated on pertinent, well-studied outcrop analogs.
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43

Handong, Tan, Tong Tuo, and Lin Changhong. "The parallel 3D magnetotelluric forward modeling algorithm." Applied Geophysics 3, no. 4 (December 2006): 197–202. http://dx.doi.org/10.1007/s11770-006-4001-5.

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44

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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45

Guan, Peixin. "Application of Forward Modeling in Putaohua Oilfield." E3S Web of Conferences 406 (2023): 01005. http://dx.doi.org/10.1051/e3sconf/202340601005.

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Putaohua Oilfield is bounded by the large fault on the south side of Pu A1 Well. It is divided into Pubei and Punan development zones on the plane. It is an oilfield that has been developed by water injection for more than 30 years. With the increase of development time, the water content of oil wells increases rapidly, and the oil production of the oilfield decreases rapidly,In order to realize the study of petroleum geological potential in this area, find out the favorable target area for well layout, and effectively improve the drilling success rate,first of all, we need to do forward modeling for the study area to lay a solid foundation for subsequent research. This paper analyzes the change characteristics of seismic wave frequency attenuation attribute in reservoir oil and gas, and applies it to the study of reservoir oil and gas prediction in Putaohua Oilfield.
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46

Deng, Fei, Jian Hu, Xuben Wang, Siling Yu, Bohao Zhang, Shuai Li, and Xue Li. "Magnetotelluric Deep Learning Forward Modeling and Its Application in Inversion." Remote Sensing 15, no. 14 (July 23, 2023): 3667. http://dx.doi.org/10.3390/rs15143667.

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Magnetotelluric (MT) inversion and forward modeling are closely linked. The optimization and iteration processes of the inverse algorithm require frequent calls to forward modeling. However, traditional numerical simulations for forward modeling are computationally expensive; here, deep learning (DL) networks can simulate forward modeling and significantly improve forward speed. Applying DL for forward modeling in inversion problems requires a high-precision network capable of responding to fine changes in the model to achieve high accuracy in inversion optimization. Most existing MT studies have used a convolutional neural network, but this method is limited by the receptive field and cannot extract global feature information. In contrast, the Mix Transformer has the ability to globally model and extract features. In this study, we used a Mix Transformer to hierarchically extract feature information, adopted a multiscale approach to restore feature information to the decoder, and eliminated the skip connection between the encoder and decoder. We designed a forward modeling network model (MT-MitNet) oriented toward inversion. A sample dataset required for DL forward was established using the forward data generated from the traditional inverse calculation iteration process. The trained network quickly and accurately calculates the forward response. The experimental results indicate a high agreement between the forward results of MT-MitNet and those obtained with traditional methods. When MT-MitNet replaces the forward computation in traditional inversion, the inversion results obtained with it are also highly in agreement with the traditional inversion results. Importantly, under the premise of ensuring high accuracy, the forward speed of MT-MitNet is hundreds of times faster than that of traditional inversion methods in the same process.
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47

Hansen, Thomas Mejer, Knud Skou Cordua, Bo Holm Jacobsen, and Klaus Mosegaard. "Accounting for imperfect forward modeling in geophysical inverse problems — Exemplified for crosshole tomography." GEOPHYSICS 79, no. 3 (May 1, 2014): H1—H21. http://dx.doi.org/10.1190/geo2013-0215.1.

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Inversion of geophysical data relies on knowledge about how to solve the forward problem, that is, computing data from a given set of model parameters. In many applications of inverse problems, the solution to the forward problem is assumed to be known perfectly, without any error. In reality, solving the forward model (forward-modeling process) will almost always be prone to errors, which we referred to as modeling errors. For a specific forward problem, computation of crosshole tomographic first-arrival traveltimes, we evaluated how the modeling error, given several different approximate forward models, can be more than an order of magnitude larger than the measurement uncertainty. We also found that the modeling error is strongly linked to the spatial variability of the assumed velocity field, i.e., the a priori velocity model. We discovered some general tools by which the modeling error can be quantified and cast into a consistent formulation as an additive Gaussian observation error. We tested a method for generating a sample of the modeling error due to using a simple and approximate forward model, as opposed to a more complex and correct forward model. Then, a probabilistic model of the modeling error was inferred in the form of a correlated Gaussian probability distribution. The key to the method was the ability to generate many realizations from a statistical description of the source of the modeling error, which in this case is the a priori model. The methodology was tested for two synthetic ground-penetrating radar crosshole tomographic inverse problems. Ignoring the modeling error can lead to severe artifacts, which erroneously appear to be well resolved in the solution of the inverse problem. Accounting for the modeling error leads to a solution of the inverse problem consistent with the actual model. Further, using an approximate forward modeling may lead to a dramatic decrease in the computational demands for solving inverse problems.
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48

Hu, Shuang Xi. "Study on Parametric Reverse Modeling." Applied Mechanics and Materials 721 (December 2014): 230–33. http://dx.doi.org/10.4028/www.scientific.net/amm.721.230.

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Methods about parametric reverse modeling have been studied based on products’ point clouds by comparing model rebuilding processes in different ways, such as collaborative reconstruction based on both reverse engineering software Geomagic and 3D modeling software, reverse and forward hybrid modeling based on Rapidform. It‘s concluded that reverse and forward hybrid modeling based on Rapidform takes more advantages in parametric reverse modeling, It is more rapid , accurate, and closer to the design intent.
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49

Dchieche, Amina. "Pricing of Participating Forward Contract." Global Review of Islamic Economics and Business 8, no. 2 (December 19, 2020): 091. http://dx.doi.org/10.14421/grieb.2020.082-03.

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The purpose of this work is to model a participating forward contract permitting to avoid unlimited risk and unknown loss using a formula of risk sharing that includes the payment of an additional amount under specific price variations. This contract offers a new tool that Islamic finance can use since this finance is suspicious of classical forward contracts. The modeling is based on the classical forward equation, which incorporates the profit and loss sharing principle derived from Islamic finance. The participating forward is tested on oil data prices to compare the participating forward contract to the classical one. The participating forward offers a better possibility of profit to the seller and the buyer because of the PLS mechanism which reduces the risk for both parties. The main implication of this modeling is that the participating forward can provide some investors and Islamic banks with an alternative to conventional forward contracts.
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50

Yan, Bo, Xinwu Zeng, and Yuan Li. "Subsection Forward Modeling Method of Blasting Stress Wave Underground." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/678468.

Full text
Abstract:
The generation of stress waves induced by explosions underground is governed by material nonlinear responses of materials surrounding explosions and affected by source region mediums and local structures. A nonlinear finite element (NFE) method can simulate the generation efficiently. However, the calculation using the NFE to observational distances, where motions are elastic, is computationally challenging. In order to tackle this problem, we present a subsection numerical simulating method for forward modelling the generation and propagation of stress waves with a hybrid method coupling the NFE and a linear finite element (LFE). The subsection idea is developed based on previous works; calculating steps of the subsection method as well as techniques of passing motions from a source region to an elastic region are discussed. 3D numerical simulations of stress wave propagation in rock generated by decoupled explosion underground with two methods for comparison are carried out. The accuracy of the subsection method is demonstrated with simulated results. The demand of PC memory and the calculating time are investigated. The subsection method provides another approach for modeling and understanding the generation and propagation of explosion-induced stress waves, though, currently, studies are preliminary.
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