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About 10,000 papers for “inverse method”
Wei Wang, Xue Xing Heng, Liu Ru Xun · Inverse Problems · 1987
An exact inverse method-the filtering inverse method-is proposed for the nonequal travel-time layered model, and a set of recurrence formulae has been given for computing the reflection coefficient rk and impedance eta k of each interface.
Cited by 1
WORLD SCIENTIFIC eBooks · 1999
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Étienne André, Romain Soulat · 2013
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André, Étienne, Soulat, Romain · HAL (Le Centre pour la Communication Scientifique Directe) · 2013
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Degtyarev, Anatoli · Research Explorer (The University of Manchester) · 2001
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2013
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O. Melchert, W. Scheid, B. Apagyi · 2006
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Anatoli Degtyarev, Andrei Voronkov, Second Readers Ullrich Hustadt et al. · 2001
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Anthony J. Culyer · 2014
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André, Étienne, Soulat, Romain · HAL (Le Centre pour la Communication Scientifique Directe) · 2013
International audience
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Alexander Degtyarev, Андрей Воронков · Elsevier eBooks · 2001
Cited by 52
Degtyarev, Anatoli · Research Explorer (The University of Manchester) · 2001
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Niek Kusters, Sjoerd Groeskamp, Trevor J. McDougall · Journal of Physical Oceanography · 2024
Abstract Here, we introduce the Spiralling Inverse Method (SIM) that provides estimates of the small-scale and mesoscale mixing strength. The SIM uses a vertical integral over a balance between the water mass transformation equation and the thermal wind equation. The result is an equation where all terms, except for the mixing strengths, can be obtained from hydrographic data of temperature and salinity. As an advantage, the SIM estimates the mixing strengths without the need for further knowledge of a reference velocity or streamfunction. Here, we apply the SIM to a small region in the North Atlantic. We find that the estimates obtained by the SIM compare well to observations and other (inverse) estimates of the mixing strength. The SIM therefore has the potential to improve and constrain parameterizations used for climate and ecosystem modeling using readily available hydrographic data. Significance Statement Ocean mixing is a combination of many different physical processes over a large range of scales in time (from seconds to years) and space (from millimeters to 100 km). Most of these processes are too small to compute in climate models and need to be simplified (parameterized). These parameterizations have a strong influence on climate projections, and their shape and magnitude need to be constrained using observations and indirect estimates of ocean mixing strength. The Spiralling Inverse Method (SIM) is a new method to obtain such constraints of mixing strengths using readily available observations of temperature and salinity. We here test the SIM and confirm its potential to improve mixing estimates and therewith ultimately climate simulations.
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Yudi Pawitan, Youngjo Lee · 2024
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Dadarlat, D., Chirtoc, M., Candea, R. et al. · Data Archiving and Networked Services (DANS) · 1990
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Minoru Fujimoto · Morgan & Claypool Publishers eBooks · 2014
Nonlinear physics is a well-established discipline in physics today, and this book offers a comprehensive account of the basic soliton theory and its applications. Although primarily mathematical, the theory for nonlinear phenomena in practical environments needs to be understood at upper undergraduate level, with particular attention given to the presence of media where nonlinearity takes place. This book addresses mathematical theories, but also suggests possible theoretical innovations for many issues, providing a stimulating reference for both students and researchers.
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D. D dârlat, M. Chirtoc, C. Nema u et al. · physica status solidi (a) · 1990
Cited by 64
Saheed O. Ojo, Hasan M. Khalid, Aniket Gopa Chanda et al. · 2025
2.1 Introduction Problems in engineering are typically governed by a system of high-order differential equations subject to various constraints, typically at boundaries, which require efficient numerical methods to provide reliable solutions. The conventional approach by direct approximation of system variables can potentially incur considerable error due to high sensitivity of high-order numerical differentiation to noise, thus necessitating improved techniques that can better satisfy the requirements of numerical accuracy desirable in solution of high-order systems. In this regard, this chapter presents a mathematical framework of a differential quadrature method based inverse approximation known as the inverse differential quadrature method (iDQM) for one- and two-dimensional systems. A computationally efficient technique that circumvents the complexities of analytical integration and Gaussian integration is fully described to obtain the iDQM weighting coefficients based on the inversion of the existing differential quadrature method (DQM) formula. Detailed derivations of the iDQM error formulation are also provided to demonstrate the qualities of iDQM. Finally, iDQM is implemented and assessed for its use to approximate functions as well as for solutions of high-order ordinary (ODEs) and partial differential equations (PDEs).
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Laurent Fribourg, Ulrich Kühne, École Normale et al. · 2011
Abstract. Hybrid systems combine continuous and discrete behavior. Hybrid Automata are a powerful formalism for the modeling and verification of such systems. A common problem in hybrid system verification is the good parameters problem, which consists in identifying a subset of parameters which guarantee a certain behavior of a system. Recently, a method has been presented for attacking this problem for Timed Automata. In this report, we show the extension of this methodology for hybrid automata with linear and affine dynamics. The method is demonstrated with a distributed temperature control system and several other hybrid system benchmarks from the literature. 1
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De Villiers, Jean Schepers · Unisa Institutional Repository (University of South Africa) · 2010
The problem of fitting a material property of the earth to a certain model by \nanalysing a returned seismic signal is investigated here. Analysis proceeds with \nmethods taken from the theory of inverse problems. Seismic wave inversion is tack- \nled by minimisation of the objective function with respect to the model parameters. \nAbsorbing boundary conditions are implemented using an exponentially decaying \nansatz.
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