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The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.

supervised machine learning --- proper orthogonal decomposition (POD) --- PGD compression --- stabilization --- nonlinear reduced order model --- gappy POD --- symplectic model order reduction --- neural network --- snapshot proper orthogonal decomposition --- 3D reconstruction --- microstructure property linkage --- nonlinear material behaviour --- proper orthogonal decomposition --- reduced basis --- ECSW --- geometric nonlinearity --- POD --- model order reduction --- elasto-viscoplasticity --- sampling --- surrogate modeling --- model reduction --- enhanced POD --- archive --- modal analysis --- low-rank approximation --- computational homogenization --- artificial neural networks --- unsupervised machine learning --- large strain --- reduced-order model --- proper generalised decomposition (PGD) --- a priori enrichment --- elastoviscoplastic behavior --- error indicator --- computational homogenisation --- empirical cubature method --- nonlinear structural mechanics --- reduced integration domain --- model order reduction (MOR) --- structure preservation of symplecticity --- heterogeneous data --- reduced order modeling (ROM) --- parameter-dependent model --- data science --- Hencky strain --- dynamic extrapolation --- tensor-train decomposition --- hyper-reduction --- empirical cubature --- randomised SVD --- machine learning --- inverse problem plasticity --- proper symplectic decomposition (PSD) --- finite deformation --- Hamiltonian system --- DEIM --- GNAT

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Wildfire risk can be perceived as the combination of wildfire hazards (often described by likelihood and intensity) with the susceptibility of people, property, or other valued resources to that hazard. Reflecting the seriousness of wildfire risk to communities around the world, substantial resources are devoted to assessing wildfire hazards and risks. Wildfire hazard and risk assessments are conducted at a wide range of scales, from localized to nationwide, and are often intended to communicate and support decision making about risks, including the prioritization of scarce resources. Improvements in the underlying science of wildfire hazard and risk assessment and in the development, communication, and application of these assessments support effective decisions made on all aspects of societal adaptations to wildfire, including decisions about the prevention, mitigation, and suppression of wildfire risks. To support such efforts, this Special Issue of the journal Fire compiles articles on the understanding, modeling, and addressing of wildfire risks to homes, water resources, firefighters, and landscapes.

wildfire risk --- object-oriented image analysis --- Sentinel-2 --- fire behavior --- flammap --- wildfire management --- water supply --- erosion --- wildfire containment --- Potential fire Operational Delineations --- Monte Carlo simulation --- transmission risk --- WUI --- fire --- defensible space --- prescribed fire --- community vulnerability --- fire suppression costs --- Zillow --- wildfire --- predictive modeling --- fire spread model --- Monte Carlo --- spatial modeling --- area difference index --- statistics --- precision --- recall --- principal components analysis --- risk assessment --- structure loss --- wildland–urban interface --- mitigation --- mapping --- land use --- disaster --- fire spread models --- surrogate modeling --- sensitivity analysis --- global sensitivity analysis --- colour coding --- communication --- forest fire --- ordinal categorization --- palette --- risk --- firefighter safety --- safe separation distance --- safety zones --- LCES --- Google Earth Engine --- lidar --- LANDFIRE --- Landsat --- GEDI --- parcel-level risk --- post-fire analysis --- risk mitigation --- rapid assessment --- natural hazards --- fuels --- fire hazard --- remote sensing --- LiDAR --- Sentinel --- modeling --- simulation

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Recent advancements in computer technology have allowed for designers to have direct control over the production process through the help of computer-based tools, creating the possibility of a completely integrated design and manufacturing process. Over the last few decades, "artificial intelligence" (AI) techniques, such as machine learing and deep learning, have been topics of interest in computer-based design and manufacturing research fields. However, efforts to develop computer-based AI to handle big data in design and manufacturing have not yet been successful. This Special Issue aims to collect novel articles covering artificial intelligence-based design, manufacturing, and data-driven design. It will comprise academics, researchers, mechanical, manufacturing, production and industrial engineers and professionals related to engineering design and manufacturing.

History of engineering & technology --- product service system (PSS) --- availability --- field repair kit --- gradient-based algorithm --- robust genetic algorithm --- warpage --- design of experiments --- fringe pattern --- birefringence --- automatic design --- intelligent optimization method --- CFD --- fluid machinery --- pumps --- multi-function console --- data-driven design --- mismatch equation --- anthropometric measures --- algorithmic approach --- optimal design --- stretchable antenna-based strain sensor --- structural optimization --- structural health monitoring --- dimension reduction --- entropy-based correlation coefficient --- multidisciplinary design and analysis --- uncertainty-integrated and machine learning-based surrogate modeling --- additive manufacturing --- complexity --- modular design --- part consolidation --- product recovery --- product image design --- Kansei Engineering --- integrated decision system --- qualitative decision model --- quantitative decision model --- train seats --- measurement-assisted assembly --- coordination space --- assemblability --- small displacement torsor --- Kriging --- lower confidence bounding --- entropy theory --- product design --- simulation-based design optimization --- convolutional neural network --- object detection --- piping and instrument diagram --- unsupervised learning --- product service system (PSS) --- availability --- field repair kit --- gradient-based algorithm --- robust genetic algorithm --- warpage --- design of experiments --- fringe pattern --- birefringence --- automatic design --- intelligent optimization method --- CFD --- fluid machinery --- pumps --- multi-function console --- data-driven design --- mismatch equation --- anthropometric measures --- algorithmic approach --- optimal design --- stretchable antenna-based strain sensor --- structural optimization --- structural health monitoring --- dimension reduction --- entropy-based correlation coefficient --- multidisciplinary design and analysis --- uncertainty-integrated and machine learning-based surrogate modeling --- additive manufacturing --- complexity --- modular design --- part consolidation --- product recovery --- product image design --- Kansei Engineering --- integrated decision system --- qualitative decision model --- quantitative decision model --- train seats --- measurement-assisted assembly --- coordination space --- assemblability --- small displacement torsor --- Kriging --- lower confidence bounding --- entropy theory --- product design --- simulation-based design optimization --- convolutional neural network --- object detection --- piping and instrument diagram --- unsupervised learning

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This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.

Research & information: general --- Mathematics & science --- robust optimization --- differential evolution --- ROOT --- optimization framework --- drainage rehabilitation --- overflooding --- pipe breaking --- VCO --- CMOS differential pair --- PVT variations --- Monte Carlo analysis --- multi-objective optimization --- Pareto Tracer --- continuation --- constraint handling --- surrogate modeling --- multiobjective optimization --- evolutionary algorithms --- kriging method --- ensemble method --- adaptive algorithm --- liquid storage tanks --- base excitation --- artificial intelligence --- Multi-Gene Genetic Programming --- computational fluid dynamics --- finite volume method --- JSSP --- CMOSA --- CMOTA --- chaotic perturbation --- fixed point arithmetic --- FP16 --- pseudo random number generator --- incorporation of preferences --- multi-criteria classification --- decision-making process --- multi-objective evolutionary optimization --- outranking relationships --- decision maker profile --- profile assessment --- region of interest approximation --- optimization using preferences --- hybrid evolutionary approach --- forecasting --- Convolutional Neural Network --- LSTM --- COVID-19 --- deep learning --- trust region methods --- multiobjective descent --- derivative-free optimization --- radial basis functions --- fully linear models --- decision making process --- cognitive tasks --- recommender system --- project portfolio selection problem --- usability evaluation --- multi-objective portfolio optimization problem --- trapezoidal fuzzy numbers --- density estimators --- steady state algorithms --- protein structure prediction --- Hybrid Simulated Annealing --- Template-Based Modeling --- structural biology --- Metropolis --- optimization --- linear programming --- energy central --- robust optimization --- differential evolution --- ROOT --- optimization framework --- drainage rehabilitation --- overflooding --- pipe breaking --- VCO --- CMOS differential pair --- PVT variations --- Monte Carlo analysis --- multi-objective optimization --- Pareto Tracer --- continuation --- constraint handling --- surrogate modeling --- multiobjective optimization --- evolutionary algorithms --- kriging method --- ensemble method --- adaptive algorithm --- liquid storage tanks --- base excitation --- artificial intelligence --- Multi-Gene Genetic Programming --- computational fluid dynamics --- finite volume method --- JSSP --- CMOSA --- CMOTA --- chaotic perturbation --- fixed point arithmetic --- FP16 --- pseudo random number generator --- incorporation of preferences --- multi-criteria classification --- decision-making process --- multi-objective evolutionary optimization --- outranking relationships --- decision maker profile --- profile assessment --- region of interest approximation --- optimization using preferences --- hybrid evolutionary approach --- forecasting --- Convolutional Neural Network --- LSTM --- COVID-19 --- deep learning --- trust region methods --- multiobjective descent --- derivative-free optimization --- radial basis functions --- fully linear models --- decision making process --- cognitive tasks --- recommender system --- project portfolio selection problem --- usability evaluation --- multi-objective portfolio optimization problem --- trapezoidal fuzzy numbers --- density estimators --- steady state algorithms --- protein structure prediction --- Hybrid Simulated Annealing --- Template-Based Modeling --- structural biology --- Metropolis --- optimization --- linear programming --- energy central