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Artykuły w czasopismach na temat "Probabilistic representation of PDEs"
BERNAL, FRANCISCO, GONÇALO DOS REIS i GREIG SMITH. "Hybrid PDE solver for data-driven problems and modern branching". European Journal of Applied Mathematics 28, nr 6 (22.05.2017): 949–72. http://dx.doi.org/10.1017/s0956792517000109.
Pełny tekst źródłaBLOMKER, D., M. ROMITO i R. TRIBE. "A probabilistic representation for the solutions to some non-linear PDEs using pruned branching trees". Annales de l'Institut Henri Poincare (B) Probability and Statistics 43, nr 2 (marzec 2007): 175–92. http://dx.doi.org/10.1016/j.anihpb.2006.02.001.
Pełny tekst źródłaGevorkyan, Ashot S., Aleksander V. Bogdanov, Vladimir V. Mareev i Koryun A. Movsesyan. "Theoretical and Numerical Study of Self-Organizing Processes in a Closed System Classical Oscillator and Random Environment". Mathematics 10, nr 20 (18.10.2022): 3868. http://dx.doi.org/10.3390/math10203868.
Pełny tekst źródłaYan, Long, Bohang Xu i Zhangjun Liu. "Dimension Reduction Method-Based Stochastic Wind Field Simulations for Dynamic Reliability Analysis of Communication Towers". Buildings 13, nr 10 (16.10.2023): 2608. http://dx.doi.org/10.3390/buildings13102608.
Pełny tekst źródłaRen, Panpan, i Feng-Yu Wang. "Space-distribution PDEs for path independent additive functionals of McKean–Vlasov SDEs". Infinite Dimensional Analysis, Quantum Probability and Related Topics 23, nr 03 (wrzesień 2020): 2050018. http://dx.doi.org/10.1142/s0219025720500186.
Pełny tekst źródłaXiao, Lishun, Shengjun Fan i Dejian Tian. "A probabilistic approach to quasilinear parabolic PDEs with obstacle and Neumann problems". ESAIM: Probability and Statistics 24 (2020): 207–26. http://dx.doi.org/10.1051/ps/2019023.
Pełny tekst źródłaHaneche, M., K. Djaballah i K. Khaldi. "An algorithm for probabilistic solution of parabolic PDEs". Sequential Analysis 40, nr 4 (2.10.2021): 441–65. http://dx.doi.org/10.1080/07474946.2021.2010403.
Pełny tekst źródłaDenis, Laurent, Anis Matoussi i Jing Zhang. "Quasilinear Stochastic PDEs with two obstacles: Probabilistic approach". Stochastic Processes and their Applications 133 (marzec 2021): 1–40. http://dx.doi.org/10.1016/j.spa.2020.11.002.
Pełny tekst źródłaMatoussi, Anis, Dylan Possamaï i Wissal Sabbagh. "Probabilistic interpretation for solutions of fully nonlinear stochastic PDEs". Probability Theory and Related Fields 174, nr 1-2 (10.07.2018): 177–233. http://dx.doi.org/10.1007/s00440-018-0859-4.
Pełny tekst źródłaSow *, A. B., i E. Pardoux. "Probabilistic interpretation of a system of quasilinear parabolic PDEs". Stochastics and Stochastic Reports 76, nr 5 (październik 2004): 429–77. http://dx.doi.org/10.1080/10451120412331303150.
Pełny tekst źródłaRozprawy doktorskie na temat "Probabilistic representation of PDEs"
Izydorczyk, Lucas. "Probabilistic backward McKean numerical methods for PDEs and one application to energy management". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAE008.
Pełny tekst źródłaThis thesis concerns McKean Stochastic Differential Equations (SDEs) to representpossibly non-linear Partial Differential Equations (PDEs). Those depend not onlyon the time and position of a given particle, but also on its probability law. In particular, we treat the unusual case of Fokker-Planck type PDEs with prescribed final data. We discuss existence and uniqueness for those equations and provide a probabilistic representation in the form of McKean type equation, whose unique solution corresponds to the time-reversal dynamics of a diffusion process.We introduce the notion of fully backward representation of a semilinear PDE: thatconsists in fact in the coupling of a classical Backward SDE with an underlying processevolving backwardly in time. We also discuss an application to the representationof Hamilton-Jacobi-Bellman Equation (HJB) in stochastic control. Based on this, we propose a Monte-Carlo algorithm to solve some control problems which has advantages in terms of computational efficiency and memory whencompared to traditional forward-backward approaches. We apply this method in the context of demand side management problems occurring in power systems. Finally, we survey the use of generalized McKean SDEs to represent non-linear and non-conservative extensions of Fokker-Planck type PDEs
Sabbagh, Wissal. "Some Contributions on Probabilistic Interpretation For Nonlinear Stochastic PDEs". Thesis, Le Mans, 2014. http://www.theses.fr/2014LEMA1019/document.
Pełny tekst źródłaThe objective of this thesis is to study the probabilistic representation (Feynman-Kac for- mula) of different classes ofStochastic Nonlinear PDEs (semilinear, fully nonlinear, reflected in a domain) by means of backward doubly stochastic differential equations (BDSDEs). This thesis contains four different parts. We deal in the first part with the second order BDS- DEs (2BDSDEs). We show the existence and uniqueness of solutions of 2BDSDEs using quasi sure stochastic control technics. The main motivation of this study is the probabilistic representation for solution of fully nonlinear SPDEs. First, under regularity assumptions on the coefficients, we give a Feynman-Kac formula for classical solution of fully nonlinear SPDEs and we generalize the work of Soner, Touzi and Zhang (2010-2012) for deterministic fully nonlinear PDE. Then, under weaker assumptions on the coefficients, we prove the probabilistic representation for stochastic viscosity solution of fully nonlinear SPDEs. In the second part, we study the Sobolev solution of obstacle problem for partial integro-differentialequations (PIDEs). Specifically, we show the Feynman-Kac formula for PIDEs via reflected backward stochastic differentialequations with jumps (BSDEs). Specifically, we establish the existence and uniqueness of the solution of the obstacle problem, which is regarded as a pair consisting of the solution and the measure of reflection. The approach is based on stochastic flow technics developed in Bally and Matoussi (2001) but the proofs are more technical. In the third part, we discuss the existence and uniqueness for RBDSDEs in a convex domain D without any regularity condition on the boundary. In addition, using the approach based on the technics of stochastic flow we provide the probabilistic interpretation of Sobolev solution of a class of reflected SPDEs in a convex domain via RBDSDEs. Finally, we are interested in the numerical solution of BDSDEs with random terminal time. The main motivation is to give a probabilistic representation of Sobolev solution of semilinear SPDEs with Dirichlet null condition. In this part, we study the strong approximation of this class of BDSDEs when the random terminal time is the first exit time of an SDE from a cylindrical domain. Thus, we give bounds for the discrete-time approximation error.. We conclude this part with numerical tests showing that this approach is effective
Tan, Xiaolu. "Stochastic control methods for optimal transportation and probabilistic numerical schemes for PDEs". Palaiseau, Ecole polytechnique, 2011. https://theses.hal.science/docs/00/66/10/86/PDF/These_TanXiaolu.pdf.
Pełny tekst źródłaThis thesis deals with the numerical methods for a fully nonlinear degenerate parabolic partial differential equations (PDEs), and for a controlled nonlinear PDEs problem which results from a mass transportation problem. The manuscript is divided into four parts. In a first part of the thesis, we are interested in the necessary and sufficient condition of the monotonicity of finite difference thêta-scheme for a one-dimensional diffusion equations. An explicit formula is given in case of the heat equation, which is weaker than the classical Courant-Friedrichs-Lewy (CFL) condition. In a second part, we consider a fully nonlinear degenerate parabolic PDE and propose a splitting scheme for its numerical resolution. The splitting scheme combines a probabilistic scheme and the semi-Lagrangian scheme, and in total, it can be viewed as a Monte-Carlo scheme for PDEs. We provide a convergence result as well as a rate of convergence. In the third part of the thesis, we study an optimal mass transportation problem. The mass is transported by the controlled drift-diffusion dynamics, and the associated cost depends on the trajectories, the drift as well as the diffusion coefficient of the dynamics. We prove a strong duality result for the transportation problem, thus extending the Kantorovich duality to our context. The dual formulation maximizes a value function on the space of all bounded continuous functions, and every value function corresponding to a bounded continuous function is the solution to a stochastic control problem. In the Markovian cases, we prove the dynamic programming principle of the optimal control problems, and we propose a gradient-projection algorithm for the numerical resolution of the dual problem, and provide a convergence result. Finally, in a fourth part, we continue to develop the dual approach of mass transportation problem with its applications in the computation of the model-independent no-arbitrage price bound of the variance option in a vanilla-liquid market. After a first analytic approximation, we propose a gradient-projection algorithm to approximate the bound as well as the corresponding static strategy in vanilla options
Helmkay, Owen. "Information representation, problem format, and mental algorithms in probabilistic reasoning". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ66153.pdf.
Pełny tekst źródłaTarrago, Pierre. "Non-commutative generalization of some probabilistic results from representation theory". Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1123/document.
Pełny tekst źródłaThe subject of this thesis is the non-commutative generalization of some probabilistic results that occur in representation theory. The results of the thesis are divided into three different parts. In the first part of the thesis, we classify all unitary easy quantum groups whose intertwiner spaces are described by non-crossing partitions, and develop the Weingarten calculus on these quantum groups. As an application of the previous work, we recover the results of Diaconis and Shahshahani on the unitary group and extend those results to the free unitary group. In the second part of the thesis, we study the free wreath product. First, we study the free wreath product with the free symmetric group by giving a description of the intertwiner spaces: several probabilistic results are deduced from this description. Then, we relate the intertwiner spaces of a free wreath product with the free product of planar algebras, an object which has been defined by Bisch and Jones. This relation allows us to prove the conjecture of Banica and Bichon. In the last part of the thesis, we prove that the minimal and the Martin boundaries of a graph introduced by Gnedin and Olshanski are the same. In order to prove this, we give some precise estimates on the uniform standard filling of a large ribbon Young diagram. This yields several asymptotic results on the filling of large ribbon Young diagrams
Ugail, Hassan, i Eyad Elyan. "Efficient 3D data representation for biometric applications". IOS Press, 2007. http://hdl.handle.net/10454/2683.
Pełny tekst źródłaAn important issue in many of today's biometric applications is the development of efficient and accurate techniques for representing related 3D data. Such data is often available through the process of digitization of complex geometric objects which are of importance to biometric applications. For example, in the area of 3D face recognition a digital point cloud of data corresponding to a given face is usually provided by a 3D digital scanner. For efficient data storage and for identification/authentication in a timely fashion such data requires to be represented using a few parameters or variables which are meaningful. Here we show how mathematical techniques based on Partial Differential Equations (PDEs) can be utilized to represent complex 3D data where the data can be parameterized in an efficient way. For example, in the case of a 3D face we show how it can be represented using PDEs whereby a handful of key facial parameters can be identified for efficient storage and verification.
Shen, Amelia H. (Amelia Huimin). "Probabilistic representation and manipulation of Boolean functions using free Boolean diagrams". Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/34087.
Pełny tekst źródłaIncludes bibliographical references (p. 145-149).
by Amelia Huimin Shen.
Ph.D.
Ugail, Hassan, i S. Kirmani. "Shape reconstruction using partial differential equations". World Scientific and Engineering Academy and Society (WSEAS), 2006. http://hdl.handle.net/10454/2645.
Pełny tekst źródłaVasudevan, Shrihari. "Spatial cognition for mobile robots : a hierarchical probabilistic concept-oriented representation of space". Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17612.
Pełny tekst źródłaLloyd, James Robert. "Representation, learning, description and criticism of probabilistic models with applications to networks, functions and relational data". Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709264.
Pełny tekst źródłaKsiążki na temat "Probabilistic representation of PDEs"
Aven, Terje. Uncertainty in risk assessment: The representation and treatment of uncertainties by probabilistic and non-probabilistic methods. Chichester, West Sussex, United Kingdom: Wiley, 2014.
Znajdź pełny tekst źródłaFisseler, Jens. Learning and modeling with probabilistic conditional logic. Heidelberg: Ios Press, 2010.
Znajdź pełny tekst źródłaAven, Terje, Enrico Zio, Piero Baraldi i Roger Flage. Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Limited, John, 2014.
Znajdź pełny tekst źródłaAven, Terje, Enrico Zio, Piero Baraldi i Roger Flage. Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Incorporated, John, 2013.
Znajdź pełny tekst źródłaUncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Incorporated, John, 2013.
Znajdź pełny tekst źródłaAven, Terje, Enrico Zio, Piero Baraldi i Roger Flage. Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Incorporated, John, 2013.
Znajdź pełny tekst źródłaClassification and Probabilistic Representation of the Positive Solutions of a Semilinear Elliptic Equation. American Mathematical Society (AMS), 2004.
Znajdź pełny tekst źródłaBaulieu, Laurent, John Iliopoulos i Roland Sénéor. Functional Integrals and Probabilistic Amplitudes. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198788393.003.0008.
Pełny tekst źródłaHancox, J., i J. Boardman. The Impact of an Alternative Representation of the Atmosphere on the Predictions of the Probabilistic Consequence Code CONDOR (Reports). AEA Technology Plc, 1992.
Znajdź pełny tekst źródłaGrenander, Ulf, i Michael I. Miller. Pattern Theory. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780198505709.001.0001.
Pełny tekst źródłaCzęści książek na temat "Probabilistic representation of PDEs"
Bhattacharya, Rabi, i Edward Waymire. "Probabilistic Representation of Solutions to Certain PDEs". W Continuous Parameter Markov Processes and Stochastic Differential Equations, 273–86. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33296-8_15.
Pełny tekst źródłaCerf, Raphaël, i Joseba Dalmau. "Probabilistic Representation". W Probability Theory and Stochastic Modelling, 187–94. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08663-2_23.
Pełny tekst źródłaGoertzel, Ben, Matthew Iklé, Izabela Freire Goertzel i Ari Heljakka. "Knowledge Representation". W Probabilistic Logic Networks, 1–17. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76872-4_2.
Pełny tekst źródłaTouzi, Nizar. "Probabilistic Numerical Methods for Nonlinear PDEs". W Fields Institute Monographs, 189–99. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4286-8_12.
Pełny tekst źródłaSucar, Luis Enrique. "Bayesian Networks: Representation and Inference". W Probabilistic Graphical Models, 101–36. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_7.
Pełny tekst źródłaSucar, Luis Enrique. "Bayesian Networks: Representation and Inference". W Probabilistic Graphical Models, 111–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_7.
Pełny tekst źródłaBaudrit, Cédric, Didier Dubois i Hélène Fargier. "Representation of Incomplete Probabilistic Information". W Soft Methodology and Random Information Systems, 149–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44465-7_17.
Pełny tekst źródłaHommersom, Arjen. "Toward Probabilistic Analysis of Guidelines". W Knowledge Representation for Health-Care, 139–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18050-7_11.
Pełny tekst źródłaBényi, Árpád, Tadahiro Oh i Oana Pocovnicu. "On the Probabilistic Cauchy Theory for Nonlinear Dispersive PDEs". W Landscapes of Time-Frequency Analysis, 1–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05210-2_1.
Pełny tekst źródłaRosinger, Elemér E. "Parametric Representation of Functions". W Parametric Lie Group Actions on Global Generalised Solutions of Nonlinear PDEs, 17–24. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-015-9076-1_3.
Pełny tekst źródłaStreszczenia konferencji na temat "Probabilistic representation of PDEs"
Cortés, Vicente. "A holomorphic representation formula for parabolic hyperspheres". W PDEs, Submanifolds and Affine Differential Geometry. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2002. http://dx.doi.org/10.4064/bc57-0-1.
Pełny tekst źródłaGollek, Hubert. "Natural algebraic representation formulas for curves in C3". W PDEs, Submanifolds and Affine Differential Geometry. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2002. http://dx.doi.org/10.4064/bc57-0-8.
Pełny tekst źródłaGollek, Hubert. "Algebraic representation formulas for null curves in Sl(2,C)". W PDEs, Submanifolds and Affine Differential Geometry. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2005. http://dx.doi.org/10.4064/bc69-0-18.
Pełny tekst źródła"Probabilistic Models for Semantic Representation". W The 1st International Workshop on Ontology for e-Technologies. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002222100130022.
Pełny tekst źródłaShivakumar, Sachin, Amritam Das i Matthew M. Peet. "Representation of linear PDEs with spatial integral terms as Partial Integral Equations". W 2023 American Control Conference (ACC). IEEE, 2023. http://dx.doi.org/10.23919/acc55779.2023.10156465.
Pełny tekst źródłaWu, Haoyi, i Kewei Tu. "Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation". W Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-acl.482.
Pełny tekst źródłaDa Silva, José L., Mohamed Erraoui i Habib Ouerdiane. "Convolution Equation: Solution and Probabilistic Representation". W Proceedings of the 29th Conference. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814295437_0016.
Pełny tekst źródłaJagt, Declan S., i Matthew M. Peet. "A PIE Representation of Coupled Linear 2D PDEs and Stability Analysis using LPIs". W 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867684.
Pełny tekst źródłaJagt, Declan, Peter Seiler i Matthew Peet. "A PIE Representation of Scalar Quadratic PDEs and Global Stability Analysis Using SDP". W 2023 62nd IEEE Conference on Decision and Control (CDC). IEEE, 2023. http://dx.doi.org/10.1109/cdc49753.2023.10384073.
Pełny tekst źródłaGanzha, V. G., i E. V. Vorozhtsov. "A probabilistic symbolic-numerical method for the stability analyses of difference schemes for PDEs". W the 1993 international symposium. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/164081.164084.
Pełny tekst źródłaRaporty organizacyjne na temat "Probabilistic representation of PDEs"
Sakhanenko, Nikita A., i George F. Luger. Using Structured Knowledge Representation for Context-Sensitive Probabilistic Modeling. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2008. http://dx.doi.org/10.21236/ada491876.
Pełny tekst źródłaZio, Enrico, i Nicola Pedroni. Uncertainty characterization in risk analysis for decision-making practice. Fondation pour une culture de sécurité industrielle, maj 2012. http://dx.doi.org/10.57071/155chr.
Pełny tekst źródłaZio, Enrico, i Nicola Pedroni. Literature review of methods for representing uncertainty. Fondation pour une culture de sécurité industrielle, grudzień 2013. http://dx.doi.org/10.57071/124ure.
Pełny tekst źródłaZanoni, Wladimir, Jimena Romero, Nicolás Chuquimarca i Emmanuel Abuelafia. Dealing with Hard-to-Reach Populations in Panel Data: Respondent-Driven Survey (RDS) and Attrition. Inter-American Development Bank, październik 2023. http://dx.doi.org/10.18235/0005194.
Pełny tekst źródłaWilson, D., Daniel Breton, Lauren Waldrop, Danney Glaser, Ross Alter, Carl Hart, Wesley Barnes i in. Signal propagation modeling in complex, three-dimensional environments. Engineer Research and Development Center (U.S.), kwiecień 2021. http://dx.doi.org/10.21079/11681/40321.
Pełny tekst źródłaMazzoni, Silvia, Nicholas Gregor, Linda Al Atik, Yousef Bozorgnia, David Welch i Gregory Deierlein. Probabilistic Seismic Hazard Analysis and Selecting and Scaling of Ground-Motion Records (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, listopad 2020. http://dx.doi.org/10.55461/zjdn7385.
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