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[null,"1.","Record Nr.",null,"UNINA9910829912203321"]
[null,null,"Titolo",null,"Arbitrary Lagrangian-Eulerian and fluid-structure interaction [[electronic resource] ] : numerical simulation / / edited by Mhamed Souli, David J. Benson"]
[null,null,"Pubbl/distr/stampa",null,"London, : ISTE"]
[{"$nulls":4},"Hoboken, N.J., : Wiley, 2010"]
[null,null,"ISBN",null,"1-118-55788-3"]
[{"$nulls":4},"1-118-61868-8"]
[{"$nulls":4},"1-299-31535-6"]
[{"$nulls":4},"1-118-61970-6"]
[null,null,"Descrizione fisica",null,"1 online resource (314 p.)"]
[null,null,"Collana",null,"ISTE"]
[null,null,"Disciplina",null,"620.106"]
[{"$nulls":4},"624.1/71"]
[{"$nulls":4},"624.171"]
[null,null,"Soggetti",null,"Fluid-structure interaction"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover; Arbitrary Lagrangian-Eulerian and Fluid-Structure Interaction; Title Page; Copyright Page; Table of Contents; Introduction; Chapter 1. Introduction to Arbitrary Lagrangian-Eulerian in Finite Element Methods; 1.1. Introduction; 1.2. Governing equations; 1.3. Operator splitting; 1.4. The Lagrangian step; 1.4.1. Governing equations; 1.4.2. The central difference method; 1.4.3. Element formulation; 1.4.4. Hourglass modes; 1.4.5. Stress rates; 1.4.6. Shock viscosity; 1.4.6.1. von Neumann-Richtmyer; 1.4.6.2. Standard quadratic and linear formulation; 1.4.6.3. Effect on time step size"]
[{"$nulls":4},"1.4.7. Mixture theories1.4.7.1. Mean strain rate mixture theory; 1.4.7.2. Mean stress mixture theory; 1.5. Mesh relaxation; 1.6. The Eulerian step; 1.6.1. Transport in one dimension; 1.6.2. Multidimensional transport by operator Splitting; 1.6.3. Multidimensional transport on unstructured meshes; 1.6.4. Momentum transport; 1.6.4.1. Momentum transport using a dual mesh in one dimension; 1.6.4.2. Element-centered transport in one dimension; 1.6.5. Interface reconstruction; 1.6.5.1. Lagrangian methods; 1.6.5.2. Level set methods; 1.6.5.3. Volume of fluid methods; 1.7. Future research directions"]
[{"$nulls":4},"1.8. BibliographyChapter 2. Fluid-Structure Interaction: Application to Dynamic Problems; 2.1. Introduction; 2.2. General ALE description of Navier-Stokes equations; 2.3. Fluid-structure interaction; 2.3.1. Contact algorithms for fluid-structure interaction problems; 2.3.2. Euler-Lagrange coupling; 2.3.3. Damping in the coupling; 2.4. Numerical applications; 2.4.1. Piston problem; 2.4.2. Two-dimensional slamming modeling; 2.4.2.1. Numerical approach of a two-dimensional slamming problem; 2.4.2.2. Numerical approach for rigid structure; 2.4.3. Airbag deployment; 2.4.4. Sloshing tank problem"]
[{"$nulls":4},"2.4.4.1. Analytical treatment of the sloshing problem2.4.4.2. Sloshing in a rigid tank; 2.4.4.3. Frequency analysis for sloshing; 2.4.4.4. Application to a cylindrical flexible tank subjected to seismic loading; 2.5. Conclusion; 2.6. Acknowledgments; 2.7. Bibliography; Chapter 3. Implicit Partitioned Coupling in Fluid-Structure Interaction; 3.1. Introduction; 3.2. Computational fluid mechanics; 3.2.1. Governing equations; 3.2.1.1. Incompressible flows; 3.2.1.2. Inviscid flows; 3.2.2. Finite volume discretization; 3.2.2.1. Solution algorithms; 3.3. Computational structural mechanics"]
[{"$nulls":4},"3.3.1. Governing equations3.3.1.1. Linear elasticity; 3.3.1.2. Plane stress problems; 3.3.1.3. Hyperelasticity; 3.3.2. Finite element methods; 3.4. Fluid-structure interaction algorithms; 3.4.1. ALE formulation; 3.4.2. Mesh dynamics; 3.4.2.1. Algebraic approaches; 3.4.2.2. Elliptic approaches; 3.4.3. Coupling methods; 3.4.3.1. Implicit partitioned coupling; 3.5. Results and applications; 3.5.1. Verification results; 3.5.2. Validation results; 3.5.3. Flow induced by solid deformation; 3.5.4. Interaction of flow and solid deformation; 3.6. Bibliography"]
[{"$nulls":4},"Chapter 4. Avoiding Instabilities Caused by Added Mass Effects in Fluid-Structure Interaction Problems"]
[null,null,null,"Sommario/riassunto","This book provides the fundamental basics for solving fluid structure interaction problems, and describes different algorithms and numerical methods used to solve problems where fluid and structure can be weakly or strongly coupled. These approaches are illustrated with examples arising from industrial or academic applications. Each of these approaches has its own performance and limitations. Given the book's comprehensive coverage, engineers, graduate students and researchers involved in the simulation of practical fluid structure interaction problems will find this book extremely useful."]
[null,"2.","Record Nr.",null,"UNINA9910624306803321"]
[null,null,"Autore",null,"Zhang Qingquan Tony"]
[null,null,"Titolo",null,"Alternative Data and Artificial Intelligence Techniques : Applications in Investment and Risk Management / / by Qingquan Tony Zhang, Beibei Li, Danxia Xie"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Palgrave Macmillan, , 2022"]
[null,null,"ISBN",null,"9783031116124"]
[{"$nulls":4},"3031116127"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (340 pages)"]
[null,null,"Collana",null,"Palgrave Studies in Risk and Insurance, , 2523-823X"]
[null,null,"Disciplina",null,"658.155"]
[{"$nulls":4},"332.1028563"]
[null,null,"Soggetti",null,"Financial risk management"]
[{"$nulls":4},"Financial engineering"]
[{"$nulls":4},"Valuation"]
[{"$nulls":4},"Risk Management"]
[{"$nulls":4},"Financial Technology and Innovation"]
[{"$nulls":4},"Investment Appraisal"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Chapter 1: The introduction of the portfolio management and risk evaluation  -- Chapter 2: The major trends in financial portfolio management -- Chapter 3: Machine Learning and AI in financial portfolio management -- Chapter 4: Introduction of Alternative data in Finance -- Chapter 5: Alternative Data utilization from country perspective -- Chapter 6: Smart Beta and Risk Factors based on Textural Data and Machine Learning -- Chapter 7: Smart Beta and Risk Factors based on IoTs and AIoTs Data -- Chapter 8: Environmental, Social Responsibility and Corporate Governance on Corporations -- Chapter 9: Case Study – Fraud and Deception Detection: Text-based Data Analytics  -- Chapter 10: Case Study – Investment Risk Analysis based on Sentiment Analysis and implementation  -- Chapter 11: Case Study – Analyzing the corporation performance with ESG Factors -- Chapter 12: Alternative Data Visualization in Python."]
[null,null,null,"Sommario/riassunto","This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, "]
[{"$nulls":4},"information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation. Qingquan Tony Zhang is an Adjunct Professor at the University of Illinois at Champaign, R.C. Evan Fellow, Gies Business School, focusing on finance, quantitative investment and entrepreneurship. He is President of the Chicago chapter of the Chinese American Association for Trading and Investment, who has long worked in FinTech, including artificial intelligence and big data. Beibei Li is an Associate Professor of IT & Management and Anna Loomis McCandless Chair at Carnegie Mellon University. Dr. Li has extensive experience at leveraging large-scale observational data analytics and experimental analysis with a strong focus on modeling individual user behavior across online, offline, and mobile channels for decision support. Danxia Xie is an Associate Professor in Economics at Tsinghua University, China. Dr. Xie’s teaching and research focuses on digital economy, finance, law and economics, and macroeconomics. Dr. Xie has also worked at Peterson Institute for International Economics, a top think tank at Washington, DC."]