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Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.

History of engineering & technology --- deposit insurance --- implied volatility --- static arbitrage --- parameterization --- machine learning --- calibration --- dichotomous response --- predictive model --- tree boosting --- GLM --- validation --- generalised linear modelling --- zero-inflated poisson model --- telematics --- benchmark --- cross-validation --- prediction --- stock return volatility --- long-term forecasts --- overlapping returns --- autocorrelation --- chain ladder --- Bornhuetter–Ferguson --- maximum likelihood --- exponential families --- canonical parameters --- prior knowledge --- accelerated failure time model --- chain-ladder method --- local linear kernel estimation --- non-life reserving --- operational time --- zero-inflation --- overdispersion --- automobile insurance --- risk classification --- risk selection --- least-squares monte carlo method --- proxy modeling --- life insurance --- Solvency II --- claims prediction --- export credit insurance --- semiparametric modeling --- VaR estimation --- analyzing financial data --- n/a --- Bornhuetter-Ferguson

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Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.

History of engineering & technology --- deposit insurance --- implied volatility --- static arbitrage --- parameterization --- machine learning --- calibration --- dichotomous response --- predictive model --- tree boosting --- GLM --- validation --- generalised linear modelling --- zero-inflated poisson model --- telematics --- benchmark --- cross-validation --- prediction --- stock return volatility --- long-term forecasts --- overlapping returns --- autocorrelation --- chain ladder --- Bornhuetter-Ferguson --- maximum likelihood --- exponential families --- canonical parameters --- prior knowledge --- accelerated failure time model --- chain-ladder method --- local linear kernel estimation --- non-life reserving --- operational time --- zero-inflation --- overdispersion --- automobile insurance --- risk classification --- risk selection --- least-squares monte carlo method --- proxy modeling --- life insurance --- Solvency II --- claims prediction --- export credit insurance --- semiparametric modeling --- VaR estimation --- analyzing financial data

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This book offers a collection of 17 scientific papers about the computational modeling of fracture. Some of the manuscripts propose new computational methods and/or how to improve existing cutting edge methods for fracture. These contributions can be classified into two categories: 1. Methods which treat the crack as strong discontinuity such as peridynamics, scaled boundary elements or specific versions of the smoothed finite element methods applied to fracture and 2. Continuous approaches to fracture based on, for instance, phase field models or continuum damage mechanics. On the other hand, the book also offers a wide range of applications where state-of-the-art techniques are employed to solve challenging engineering problems such as fractures in rock, glass, concrete. Also, larger systems such as fracture in subway stations due to fire, arch dams, or concrete decks are studied.

Brittle Fracture --- n/a --- microstructure --- fatigue crack growth --- fracture process zone (FPZ) --- crack shape change --- fracture network modeling --- Mohr-Coulomb --- fracture --- SBFEM --- topological insulator --- fatigue --- progressive collapse analysis --- Phase-field model --- loss of key components --- concrete creep --- compressive stress --- rail squats --- cracks --- force transfer --- rolling contact --- damage-plasticity model --- implicit gradient-enhancement --- extended scaled boundary finite element method (X-SBFEM) --- three-parameter model --- LEFM --- overall stability --- EPB shield machine --- metallic glass matrix composite --- phase field --- reinforced concrete core tube --- bulk damage --- ductility --- thermomechanical analysis --- incompatible approximation --- moderate fire --- finite element simulations --- shear failure --- FSDT --- gradient-enhanced model --- prestressing stress --- self-healing --- peridynamics --- damage-healing mechanics --- stress intensity factors --- damage --- dam stress zones --- shear band --- rock fracture --- random fracture --- surface crack --- plate --- steel reinforced concrete frame --- super healing --- brittle material --- geometric phase --- FE analysis --- grouting --- rock --- elastoplastic behavior --- parameters calibration --- screened-Poisson model --- anisotropic --- numerical simulation --- Discontinuous Galerkin --- brittle fracture --- XFEM/GFEM --- topological photonic crystal --- photonic orbital angular momentum --- conditioned sandy pebble --- yielding region --- finite element analysis --- fluid–structure interaction --- cracking risk --- Mindlin --- ABAQUS UEL --- particle element model --- HSDT --- cell-based smoothed-finite element method (CS-FEM) --- the Xulong arch dam --- fluid-structure interaction