Search results for "Mathematica"

showing 10 items of 7971 documents

Using Global Radiation Model to Simulate Surface Temperature Impact on Snow Melt - Loven Glacier – Spitsberg

2009

International audience

[ SDE.MCG ] Environmental Sciences/Global Changes[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation[SDE.MCG] Environmental Sciences/Global Changes[MATH.MATH-MP]Mathematics [math]/Mathematical Physics [math-ph][SDE.MCG]Environmental Sciences/Global Changes[ MATH.MATH-MP ] Mathematics [math]/Mathematical Physics [math-ph][SDU.STU] Sciences of the Universe [physics]/Earth Sciences[SDU.STU]Sciences of the Universe [physics]/Earth Sciences[ SDU.STU ] Sciences of the Universe [physics]/Earth Sciences[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation[MATH.MATH-MP] Mathematics [math]/Mathematical Physics [math-ph][INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationComputingMilieux_MISCELLANEOUS
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A new invariant-based method for building biomechanical behavior laws - Application to an anisotropic hyperelastic material with two fiber families

2013

Abstract In this article, we present a general constructive and original approach that allows us to calculate the invariants associated with an anisotropic hyperelastic material made of two families of collagen fibers. This approach is based on mathematical techniques from the theory of invariants: • Definition of the material symmetry group. • Analytical calculation of a set of generators using the Noether’s theorem. • Analytical calculation of an integrity basis. • Comparison between the proposed invariants and the classical ones.

[ SPI.MAT ] Engineering Sciences [physics]/Materials02 engineering and technologyTheory of invariantsConstructiveAnisotropic hyperelastic material[SPI.MAT]Engineering Sciences [physics]/Materialssymbols.namesake0203 mechanical engineeringMaterials Science(all)Modelling and SimulationGeneral Materials ScienceBiomechanicsInvariant (mathematics)AnisotropyMaterial symmetryMathematicsMechanical EngineeringApplied MathematicsMathematical analysis021001 nanoscience & nanotechnologyCondensed Matter Physics020303 mechanical engineering & transportsMechanics of MaterialsModeling and SimulationHyperelastic materialsymbolsNoether's theorem0210 nano-technology
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Some Computational Aspects of DISTANCE-SAT

2007

In many AI fields, one must face the problem of finding a solution that is as close as possible to a given configuration. This paper addresses this problem in a propositional framework. We introduce the decision problem distance-sat, which consists in determining whether a propositional formula admits a model that disagrees with a given partial interpretation on at most d variables. The complexity of distance-sat and of several restrictions of it are identified. Two algorithms based on the well-known Davis/Logemann/Loveland search procedure for the satisfiability problem sat are presented so as to solve distance-sat for CNF formulas. Their computational behaviors are compared with the ones …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Theoretical computer scienceComputational complexity theory0102 computer and information sciences02 engineering and technologyComputer Science::Computational Complexity01 natural sciences[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]#SATArtificial IntelligenceComputer Science::Logic in Computer ScienceDPLL algorithm0202 electrical engineering electronic engineering information engineeringComputingMilieux_MISCELLANEOUSMathematicsDecision problemFunction problemSatisfiabilityPropositional formulaTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESComputational Theory and Mathematics010201 computation theory & mathematics020201 artificial intelligence & image processingBoolean satisfiability problemAlgorithmSoftware
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How to Enrich Description Logics with Fuzziness

2017

International audience; The paper describes the relation between fuzzy and non-fuzzy description logics. It gives an overview about current research in these areas and describes the difference between tasks for description logics and fuzzy logics. The paper also deals with the transformation properties of description logics to fuzzy logics and backwards. While the process of transformation from a description logic to a fuzzy logic is a trivial inclusion, the other way of reducing information from fuzzy logic to description logic is a difficult task, that will be topic of future work.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Theoretical computer science[ INFO ] Computer Science [cs]Relation (database)Process (engineering)Computer scienceMathematics::General Mathematics0102 computer and information sciences02 engineering and technology[INFO] Computer Science [cs]01 natural sciencesFuzzy logicTask (project management)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Knowledge-based systemsFuzzy Description LogicDescription logicComputer Science::Logic in Computer Science0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Semantic WebSemantic WebUncertaintyTransformation (function)TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES010201 computation theory & mathematics020201 artificial intelligence & image processingComputingMethodologies_GENERALHardware_LOGICDESIGN
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Attempts to produce minimal Resolution refutations

2018

We address the challenge of searching minimal refutations proofs of inconsistent CNF formulae using the Resolution rule. We propose two algorithms which can only afford formulae of at most 5 variables with a desktop computer. A faster but incomplete algorithm is used to produce "hard" 5 variables 3CNF formulae though a stochastic greedy search. It allowed us to find formulae that can be refuted by producing clauses of at most 3 literals, but whose all minimal refutations contain at least one clause of 4 literals.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES
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in Informatique graphique, modélisation géométrique et animation

2007

International audience; no abstract

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation[INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO][ INFO.INFO-NA ] Computer Science [cs]/Numerical Analysis [cs.NA][INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO][ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO][ INFO.INFO-MS ] Computer Science [cs]/Mathematical Software [cs.MS][INFO.INFO-MS] Computer Science [cs]/Mathematical Software [cs.MS][INFO.INFO-NA] Computer Science [cs]/Numerical Analysis [cs.NA][ INFO.INFO-AU ] Computer Science [cs]/Automatic Control Engineering[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-AU] Computer Science [cs]/Automatic Control EngineeringComputingMilieux_MISCELLANEOUS[ INFO.INFO-RO ] Computer Science [cs]/Operations Research [cs.RO]
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Automated uncertainty quantification analysis using a system model and data

2015

International audience; Understanding the sources of, and quantifying the magnitude of, uncertainty can improve decision-making and, thereby, make manufacturing systems more efficient. Achieving this goal requires knowledge in two separate domains: data science and manufacturing. In this paper, we focus on quantifying uncertainty, usually called uncertainty quantification (UQ). More specifically, we propose a methodology to perform UQ automatically using Bayesian networks (BN) constructed from three types of sources: a descriptive system model, physics-based mathematical models, and data. The system model is a high-level model describing the system and its parameters; we develop this model …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]generic modeling environment[SPI] Engineering Sciences [physics]Computer scienceuncertainty quantificationMachine learningcomputer.software_genre01 natural sciencesData modelingSystem model[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]010104 statistics & probability03 medical and health sciences[SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics]Sensitivity analysis0101 mathematicsUncertainty quantification[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]030304 developmental biologyautomation0303 health sciencesMathematical modelbusiness.industryConditional probabilityBayesian networkmeta-modelMetamodelingBayesian networkProbability distributionData miningArtificial intelligencebusinesscomputer
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PROCEDE DE PRE-DISTORSION NUMERIQUE D’UN SIGNAL ET REPETEUR DE TELECOMMUNICATION INTEGRANT UN FILTRE A REPONSE IMPULSIONNELLE FINIE POUR METTRE EN OE…

2013

L'invention concerne un procédé de pré-distorsion numérique d'un signal de télécommunication traité dans un circuit électronique 100 intégrant un filtre à réponse impulsionnelle finie 321. Ce procédé consiste successivement: - à identifier, à la sortie du circuit 100, les paramètres de distorsions de phase et/ou d'amplitude du signal en fonction de la fréquence, - à partir des susdits paramètres de distorsions relevés, à générer, par un algorithme basé sur une interpolation, des coefficients permettant d'effectuer dans ledit filtre 321, des prédistorsions du signal numérique destinées à engendrer une précorrection des susdites distorsions, - à transférer lesdits coefficients de pré-distorsi…

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR][SPI.OTHER]Engineering Sciences [physics]/Other[INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR][ SPI.OTHER ] Engineering Sciences [physics]/Other[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]Prédistorsion numérique[ INFO.INFO-ES ] Computer Science [cs]/Embedded SystemsRépéteurs[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS][ INFO.INFO-SI ] Computer Science [cs]/Social and Information Networks [cs.SI][SPI.OTHER] Engineering Sciences [physics]/Other[INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI]Spline[SPI.TRON] Engineering Sciences [physics]/Electronics[INFO.INFO-ES] Computer Science [cs]/Embedded Systems[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics[ INFO.INFO-MS ] Computer Science [cs]/Mathematical Software [cs.MS][INFO.INFO-MS] Computer Science [cs]/Mathematical Software [cs.MS]FIR filters[INFO.INFO-ES]Computer Science [cs]/Embedded Systems[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR][SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingFpga
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Scheduling independent stochastic tasks under deadline and budget constraints

2018

This article discusses scheduling strategies for the problem of maximizing the expected number of tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The execution times of tasks follow independent and identically distributed probability laws. The main questions are how many processors to enroll and whether and when to interrupt tasks that have been executing for some time. We provide complexity results and an asymptotically optimal strategy for the problem instance with discrete probability distributions and without deadline. We extend the latter strategy for the general case with continuous distributions and a deadline and we design an ef…

[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]Mathematical optimizationOperations researchComputer science[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]Cloud computing[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]02 engineering and technologyExpected valueTheoretical Computer ScienceScheduling (computing)[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]deadline0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]schedulingComputer Science::Operating SystemsComputingMilieux_MISCELLANEOUSBudget constraint020203 distributed computingcloud platformindependent tasksbusiness.industry[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulationstochastic costAsymptotically optimal algorithmContinuous distributions[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Hardware and ArchitectureProbability distribution[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]020201 artificial intelligence & image processingInterrupt[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]businessSoftwarebudget
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On the projective geometry of entanglement and contextuality

2019

[MATH.MATH-AG] Mathematics [math]/Algebraic Geometry [math.AG]Invariant theory[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]Information quantiqueAlgebraic geometry[INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM]Théorie des invariants[PHYS.QPHY]Physics [physics]/Quantum Physics [quant-ph][MATH.MATH-MP]Mathematics [math]/Mathematical Physics [math-ph]Géométrie discrète et combinatoireGéométrie algébriqueQuantum Information[MATH.MATH-AG]Mathematics [math]/Algebraic Geometry [math.AG][MATH.MATH-MP] Mathematics [math]/Mathematical Physics [math-ph]Finite geometry[PHYS.QPHY] Physics [physics]/Quantum Physics [quant-ph]
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