03573nam 22005775 450 991036495690332120251113211928.03-030-32330-710.1007/978-3-030-32330-1(CKB)4100000010011880(MiAaPQ)EBC5992463(DE-He213)978-3-030-32330-1(PPN)242818900(EXLCZ)99410000001001188020191209d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierThe Large Flux Problem to the Navier-Stokes Equations Global Strong Solutions in Cylindrical Domains /by Joanna Rencławowicz, Wojciech M. Zajączkowski1st ed. 2019.Cham :Springer International Publishing :Imprint: Birkhäuser,2019.1 online resource (176 pages)Lecture Notes in Mathematical Fluid Mechanics,2510-13823-030-32329-3 Introduction -- Notation and auxiliary results -- Energy estimate: Global weak solutions -- Local estimates for regular solutions -- Global estimates for solutions to problem on (v, p) -- Global estimates for solutions to problem on (h, q) -- Estimates for ht -- Auxiliary results: Estimates for (v, p) -- Auxiliary results: Estimates for (h, q) -- The Neumann problem (3.6) in L2-weighted spaces -- The Neumann problem (3.6) in Lp-weighted spaces -- Existence of solutions (v, p) and (h, q).This monograph considers the motion of incompressible fluids described by the Navier-Stokes equations with large inflow and outflow, and proves the existence of global regular solutions without any restrictions on the magnitude of the initial velocity, the external force, or the flux. To accomplish this, some assumptions are necessary: The flux is close to homogeneous, and the initial velocity and the external force do not change too much along the axis of the cylinder. This is achieved by utilizing a sophisticated method of deriving energy type estimates for weak solutions and global estimates for regular solutions—an approach that is wholly unique within the existing literature on the Navier-Stokes equations. To demonstrate these results, three main steps are followed: first, the existence of weak solutions is shown; next, the conditions guaranteeing the regularity of weak solutions are presented; and, lastly, global regular solutions are proven. This volume is ideal for mathematicians whose work involves the Navier-Stokes equations, and, more broadly, researchers studying fluid mechanics.Lecture Notes in Mathematical Fluid Mechanics,2510-1382Differential equationsContinuum mechanicsFunctional analysisDifferential EquationsContinuum MechanicsFunctional AnalysisDifferential equations.Continuum mechanics.Functional analysis.Differential Equations.Continuum Mechanics.Functional Analysis.515.353Rencławowicz Joannaauthttp://id.loc.gov/vocabulary/relators/aut781807Zajączkowski Wojciech Mauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910364956903321The Large Flux Problem to the Navier-Stokes Equations2529278UNINA04682nam 22006135 450 991076846800332120251226202212.03-540-33059-310.1007/11691839(CKB)1000000000232881(SSID)ssj0000318637(PQKBManifestationID)11211588(PQKBTitleCode)TC0000318637(PQKBWorkID)10310453(PQKB)11014580(DE-He213)978-3-540-33059-2(MiAaPQ)EBC3067578(PPN)123132673(EXLCZ)99100000000023288120100301d2006 u| 0engurnn|008mamaatxtccrLearning and Adaption in Multi-Agent Systems First International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers /edited by Karl Tuyls, Pieter Jan 't Hoen, Katja Verbeeck, Sandip Sen1st ed. 2006.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2006.1 online resource (X, 217 p.) Lecture Notes in Artificial Intelligence,2945-9141 ;3898"This book contains selected and revised papers of the International Workshop on Learning and Adaptation in Multi-Agent Systems (LAMAS 2005), held at the AAMAS 2005 Conference"--Pref.3-540-33053-4 Includes bibliographical references and index.An Overview of Cooperative and Competitive Multiagent Learning -- Multi-robot Learning for Continuous Area Sweeping -- Learning Automata as a Basis for Multi Agent Reinforcement Learning -- Learning Pareto-optimal Solutions in 2x2 Conflict Games -- Unifying Convergence and No-Regret in Multiagent Learning -- Implicit Coordination in a Network of Social Drivers: The Role of Information in a Commuting Scenario -- Multiagent Traffic Management: Opportunities for Multiagent Learning -- Dealing with Errors in a Cooperative Multi-agent Learning System -- The Success and Failure of Tag-Mediated Evolution of Cooperation -- An Adaptive Approach for the Exploration-Exploitation Dilemma and Its Application to Economic Systems -- Efficient Reward Functions for Adaptive Multi-rover Systems -- Multi-agent Relational Reinforcement Learning -- Multi-type ACO for Light Path Protection.This book contains selected and revised papers of the International Workshop on Lea- ing and Adaptation in Multi-Agent Systems (LAMAS 2005), held at the AAMAS 2005 Conference in Utrecht, The Netherlands, July 26. An important aspect in multi-agent systems (MASs) is that the environment evolves over time, not only due to external environmental changes but also due to agent int- actions. For this reason it is important that an agent can learn, based on experience, and adapt its knowledge to make rational decisions and act in this changing environment autonomously. Machine learning techniques for single-agent frameworks are well established. Agents operate in uncertain environments and must be able to learn and act - tonomously. This task is, however, more complex when the agent interacts with other agents that have potentially different capabilities and goals. The single-agent case is structurally different from the multi-agent case due to the added dimension of dynamic interactions between the adaptive agents. Multi-agent learning, i.e., the ability of the agents to learn how to cooperate and compete, becomes crucial in many domains. Autonomous agents and multi-agent systems (AAMAS) is an emerging multi-disciplinary area encompassing computer science, software engineering, biology, as well as cognitive and social sciences. A t- oretical framework, in which rationality of learning and interacting agents can be - derstood, is still under development in MASs, although there have been promising ?rst results.Lecture Notes in Artificial Intelligence,2945-9141 ;3898Artificial intelligenceComputer networksArtificial IntelligenceComputer Communication NetworksArtificial intelligence.Computer networks.Artificial Intelligence.Computer Communication Networks.006.3Tuyls Karl1224423International Workshop on Learning and Adaptation in Multi-Agent SystemsMiAaPQMiAaPQMiAaPQBOOK9910768468003321Learning and adaption in multi-agent systems4187876UNINA