Search results for " Stochastic differential equations"

showing 10 items of 15 documents

How diffusivity, thermocline and incident light intensity modulate the dynamics of Deep Chlorophyll Maximum in Tyrrhenian Sea

2015

During the last few years theoretical works have shed new light and proposed new hypotheses on the mechanisms which regulate the spatio-temporal behaviour of phytoplankton communities in marine pelagic ecosystems. Despite this, relevant physical and biological issues, such as effects of the time- dependent mixing in the upper layer, competition between groups, and dynamics of non-stationary deep chlorophyll maxima, are still open questions. In this work, we analyze the spatio-temporal behaviour of five phytoplankton populations in a real marine ecosystem by using a one-dimensional reaction-diffusion-taxis model. The study is performed, taking into account the seasonal variations of environm…

Chlorophyll0106 biological sciencesLight010504 meteorology & atmospheric sciencesMixed layerlcsh:MedicineOceanographyRandom processeAtmospheric sciences01 natural scienceschemistry.chemical_compoundPhytoplanktonMediterranean SeaMarine ecosystemSpatial ecologySeawaterMarine ecosystem14. Life underwaterPhytoplankton dynamiclcsh:Science0105 earth and related environmental sciencesDeep chlorophyll maximumMultidisciplinaryEcology010604 marine biology & hydrobiologylcsh:RTemperaturePelagic zoneModels TheoreticalSpatial ecology; Marine ecosystems; Phytoplankton dynamics; Deep chlorophyll maximum; Random processes; Stochastic differential equationsSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Light intensitychemistry13. Climate actionChlorophyllPhytoplanktonStochastic differential equationsDeep chlorophyll maximumEnvironmental sciencelcsh:QThermoclineAlgorithmsResearch Article
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Simulation of BSDEs with jumps by Wiener Chaos Expansion

2016

International audience; We present an algorithm to solve BSDEs with jumps based on Wiener Chaos Expansion and Picard's iterations. This paper extends the results given in Briand-Labart (2014) to the case of BSDEs with jumps. We get a forward scheme where the conditional expectations are easily computed thanks to chaos decomposition formulas. Concerning the error, we derive explicit bounds with respect to the number of chaos, the discretization time step and the number of Monte Carlo simulations. We also present numerical experiments. We obtain very encouraging results in terms of speed and accuracy.

Statistics and ProbabilityWiener Chaos expansionDiscretizationMonte Carlo methodTime stepConditional expectation01 natural sciences010104 statistics & probabilitybackward stochastic differential equations with jumpsFOS: MathematicsApplied mathematics60H10 60J75 60H35 65C05 65G99 60H070101 mathematicsMathematicsPolynomial chaosApplied MathematicsNumerical analysis010102 general mathematicsMathematical analysista111Probability (math.PR)numerical methodCHAOS (operating system)[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Modeling and SimulationScheme (mathematics)Mathematics - Probability
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STOCHASTIC DYNAMICS OF TWO PICOPHYTOPLANKTON POPULATIONS IN A REAL MARINE ECOSYSTEM

2013

A stochastic reaction-diffusion-taxis model is analyzed to get the stationary distribution along water column of two species of picophytoplankton, that is picoeukaryotes and Prochlorococcus. The model is valid for weakly mixed waters, typical of the Mediterranean Sea. External random fluctuations are considered by adding a multiplicative Gaussian noise to the dynamical equation of the nutrient concentration. The statistical tests show that shape and magnitude of the theoretical concentration profile exhibit a good agreement with the experimental findings. Finally, we study the effects of seasonal variations on picophytoplankton groups, including an oscillating term in the auxiliary equation…

PhysicsGeneral Physics and AstronomySpatial ecology; Marine ecosystems; Phytoplankton dynamics; Deep chlorophyll maximum; Random processes; Stochastic differential equationsRandom processeSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)OceanographyStochastic dynamicsMarine ecosystemStochastic differential equationsSpatial ecologyDeep chlorophyll maximumMarine ecosystemPhytoplankton dynamic
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Donsker-Type Theorem for BSDEs: Rate of Convergence

2019

In this paper, we study in the Markovian case the rate of convergence in Wasserstein distance when the solution to a BSDE is approximated by a solution to a BSDE driven by a scaled random walk as introduced in Briand, Delyon and Mémin (Electron. Commun. Probab. 6 (2001) Art. ID 1). This is related to the approximation of solutions to semilinear second order parabolic PDEs by solutions to their associated finite difference schemes and the speed of convergence. peerReviewed

Statistics and Probability[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Markov processType (model theory)scaled random walk01 natural sciencesconvergence rate010104 statistics & probabilitysymbols.namesakeMathematics::ProbabilityConvergence (routing)FOS: MathematicsOrder (group theory)Applied mathematicsWasserstein distance0101 mathematicsDonsker's theoremstokastiset prosessitMathematicskonvergenssiProbability (math.PR)010102 general mathematicsFinite differenceRandom walk[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Rate of convergencebackward stochastic differential equationssymbolsapproksimointiDonsker’s theoremfinite difference schemedifferentiaaliyhtälötMathematics - Probability
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Dynamics of Two Picophytoplankton Groups in Mediterranean Sea: Analysis of the Deep Chlorophyll Maximum by a Stochastic Advection-Reaction-Diffusion …

2013

A stochastic advection-reaction-diffusion model with terms of multiplicative white Gaussian noise, valid for weakly mixed waters, is studied to obtain the vertical stationary spatial distributions of two groups of picophytoplankton, i.e., picoeukaryotes and Prochlorococcus, which account about for 60% of total chlorophyll on average in Mediterranean Sea. By numerically solving the equations of the model, we analyze the one-dimensional spatio-temporal dynamics of the total picophytoplankton biomass and nutrient concentration along the water column at different depths. In particular, we integrate the equations over a time interval long enough, obtaining the steady spatial distributions for th…

ChlorophyllPopulation DynamicsPopulation ModelingRandom processeAtmospheric scienceschemistry.chemical_compoundTheoretical EcologyWater columnMediterranean seaDeep chlorophyll maximumCalculusMultidisciplinaryEcologybiologyEcologyApplied MathematicsPhysicsQStatisticsRComplex SystemsStochastic differential equationsInterdisciplinary PhysicsMedicineDeep chlorophyll maximumProchlorococcusResearch ArticleChlorophyll aScienceStatistical MechanicsDifferential EquationsPhytoplanktonMarine ecosystemMediterranean SeaSpatial ecologyStatistical MethodsPhytoplankton dynamicBiologyComputerized SimulationsStochastic ProcessesPopulation BiologyAdvectionComputational BiologyRandom VariablesModels TheoreticalSpatial ecology; Marine ecosystems; Phytoplankton dynamics; Deep chlorophyll maximum; Random processes; Stochastic differential equationsProbability Theorybiology.organism_classificationMarine EnvironmentsSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Nonlinear DynamicschemistryChlorophyllComputer SciencePhytoplanktonEcosystem ModelingMathematicsEcological EnvironmentsPLoS ONE
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Stochastic models for phytoplankton dynamics in marine ecosystems

2014

In this thesis, the stochastic advection-reaction-diffusion models are analyzed to obtain the vertical stationary spatial distributions of the main groups of picophytoplankton, which account about for 80% of total chlorophyll on average in Mediterranean Sea. In Chapter 1 we give a short presentation of the experimental and phytoplanktonic data collected during different oceanographic surveys in Mediterranean Sea. In Chapter 2 we introduce the deterministic and stochastic approaches (one-population model) adopted to describe the picoeukaryotes dynamics in Sicily Channel. Moreover, numerical results for the biomass concentration are compared with experimental data by using chi-squared goodnes…

Phytoplankton dynamics Marine ecosystems Spatial ecology Deep chlorophyll maximum Random processes Stochastic differential equationsSettore FIS/03 - Fisica Della Materia
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Quadratic backward stochastic differential equations

2017

Tässä tutkielmassa analysoimme takaperoisia stokastisia differentiaaliyhtälöitä. Aloitamme esittelemällä stokastiset prosessit, Brownin liikkeen, stokastiset integraalit ja Itôn kaavan. Tämän jälkeen siirrymme tarkastelemaan stokastisia differentiaaliyhtälöitä ja lopulta takaperoisia stokastisia differentiaaliyhtälöitä. Tämän tutkielman pääaiheena on takaperoiset stokastiset differentiaaliyhtälöt kvadraattisilla oletuksilla. Näillä oletuksilla todistamme olemassaoloteoreeman ja tietyt säännöllisyysehdot takaperoisen stokastisen differentiaaliyhtälön ratkaisulle. In this thesis, we analyze backward stochastic differential equations. We begin by introducing stochastic processes, Brownian moti…

Backward Stochastic Differential EquationsStochastics
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Stochastic models for phytoplankton dynamics in Mediterranean Sea

2016

Abstract In this paper, we review some results obtained from three one-dimensional stochastic models, which were used to analyze picophytoplankton dynamics in two sites of the Mediterranean Sea. Firstly, we present a stochastic advection–reaction–diffusion model to describe the vertical spatial distribution of picoeukaryotes in a site of the Sicily Channel. The second model, which is an extended version of the first one, is used to obtain the vertical stationary profiles of two groups of picophytoplankton, i.e. Pelagophytes and Prochlorococcus, in the same marine site as in the previous case. Here, we include intraspecific competition of picophytoplanktonic groups for limiting factors, i.e.…

0106 biological sciences010504 meteorology & atmospheric sciencesStochastic modellingRandom processeAtmospheric sciences01 natural sciencesDeep chlorophyll maximum; Marine ecosystems; Phytoplankton dynamics; Random processes; Spatial ecology; Stochastic differential equations; Ecology Evolution Behavior and Systematics; Ecological ModelingStochastic differential equationMediterranean seaMarine ecosystemSpatial ecology14. Life underwaterPhytoplankton dynamicEcology Evolution Behavior and Systematics0105 earth and related environmental sciencesDeep chlorophyll maximumStochastic differential equationbiologyStochastic processEcology010604 marine biology & hydrobiologyEcological Modelingbiology.organism_classificationEcology Evolution Behavior and SystematicSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Light intensitySpatial ecologyDeep chlorophyll maximumProchlorococcusEcological Complexity
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Spatio-temporal behaviour of the deep chlorophyll maximum in Mediterranean Sea: Development of a stochastic model for picophytoplankton dynamics

2013

In this paper, by using a stochastic reaction-diffusion-taxis model, we analyze the picophytoplankton dynamics in the basin of the Mediterranean Sea, characterized by poorly mixed waters. The model includes intraspecific competition of picophytoplankton for light and nutrients. The multiplicative noise sources present in the model account for random fluctuations of environmental variables. Phytoplankton distributions obtained from the model show a good agreement with experimental data sampled in two different sites of the Sicily Channel. The results could be extended to analyze data collected in different sites of the Mediterranean Sea and to devise predictive models for phytoplankton dynam…

Stochastic modellingFOS: Physical sciencesStructural basinBiologyRandom processe01 natural sciencesIntraspecific competitionMediterranean sea0103 physical sciencesPhytoplanktonMarine ecosystemSpatial ecologyMarine ecosystem14. Life underwaterQuantitative Biology - Populations and Evolution010306 general physicsPhytoplankton dynamic010301 acousticsEcology Evolution Behavior and SystematicsDeep chlorophyll maximumEcologyEcological ModelingPopulations and Evolution (q-bio.PE)Spatial ecology; Marine ecosystems; Phytoplankton dynamics; Deep chlorophyll maximum; Random processes; Stochastic differential equationsSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Oceanography13. Climate actionPhysics - Data Analysis Statistics and ProbabilityFOS: Biological sciencesSpatial ecologyStochastic differential equationsDeep chlorophyll maximumData Analysis Statistics and Probability (physics.data-an)
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Random walk approximation of BSDEs with H{\"o}lder continuous terminal condition

2018

In this paper, we consider the random walk approximation of the solution of a Markovian BSDE whose terminal condition is a locally Hölder continuous function of the Brownian motion. We state the rate of the L2-convergence of the approximated solution to the true one. The proof relies in part on growth and smoothness properties of the solution u of the associated PDE. Here we improve existing results by showing some properties of the second derivative of u in space. peerReviewed

Statistics and Probabilitynumerical schemeHölder conditionSpace (mathematics)01 natural sciences010104 statistics & probabilityMathematics::Probability0101 mathematicsBrownian motionrandom walk approximationSecond derivativeMathematicsstokastiset prosessitSmoothness (probability theory)numeeriset menetelmät010102 general mathematicsMathematical analysisSpeed of convergenceBackward stochastic differential equationsFunction (mathematics)State (functional analysis)Random walk[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]random walk approxi-mationbackward stochastic differential equationsspeed of convergencespeed of convergence MSC codes : 65C30 60H35 60G50 65G99Mathematics - Probability
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