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Książki na temat „Multi-physics processes”

Autor: Grafiati
Data publikacji: 6 września 2023

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Sprawdź 18 najlepszych książek naukowych na temat „Multi-physics processes”.

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1

Hu, Liangbo, Xiaoqiang Gu, Junliang Tao i Annan Zhou, red. Proceedings of GeoShanghai 2018 International Conference: Multi-physics Processes in Soil Mechanics and Advances in Geotechnical Testing. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0095-0.

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2

European, Mechanics of Materials Conference (3rd 1998 Oxford England). 3rd European Mechanics of Materials Conference on Mechanics and Multi-Physics Processes in Solids: Experiments, modelling, applications : EUROMECH-MECAMAT'98, Oxford, U.K., 23-25 November, 1998. Les Ulis, France: EDP Sciences, 1999.

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3

European Mechanics of Materials Conference (3rd 1998 Oxford, England). 3rd European Mechanics of Materials Conference on Mechanics and Multi-Physics Processes in Solids: Experiments, modelling, applications : EUROMECH-MECAMAT'98, Oxford, U.K., 23-25 November, 1998. Les Ulis, France: EDP Sciences, 1999.

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4

Sandro, Fuzzi, i Wagenbach Dietmar, red. Cloud multi-phase processes and high alpine air and snow chemistry: Ground-based cloud experiments and pollutant deposition in the high Alps. Berlin: Springer, 1997.

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5

Rieutord, Michel, Isabelle Baraffe i Yveline Lebreton. Multi-Dimensional Processes In Stellar Physics. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-2437-3.

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Baraffe, Isabelle, Yveline Lebreton i Michel Rieutord. Multi-Dimensional Processes in Stellar Physics: Evry Schatzman School 2018. EDP Sciences, 2021.

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7

Tao, Junliang, Xiaoqiang Gu i Liangbo Hu. Proceedings of GeoShanghai 2018 International Conference: Multi-physics Processes in Soil Mechanics and Advances in Geotechnical Testing. Springer, 2018.

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8

Zhou, Annan, Junliang Tao, Xiaoqiang Gu i Liangbo Hu. Proceedings of GeoShanghai 2018 International Conference: Multi-physics Processes in Soil Mechanics and Advances in Geotechnical Testing. Springer, 2019.

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9

Zhou, Annan, Junliang Tao, Xiaoqiang Gu i Liangbo Hu. Proceedings of GeoShanghai 2018 International Conference: Multi-physics Processes in Soil Mechanics and Advances in Geotechnical Testing. Springer, 2018.

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10

Leubner, Manfred P., i Zoltán Vörös. Multi-scale Dynamical Processes in Space and Astrophysical Plasmas. Springer, 2012.

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11

Leubner, Manfred P., i Zoltán Vörös. Multi-Scale Dynamical Processes in Space and Astrophysical Plasmas. Springer Berlin / Heidelberg, 2014.

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12

Multi-Scale Dynamical Processes in Space and Astrophysical Plasmas. Springer London, Limited, 2012.

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13

Sanderson, Benjamin Mark. Uncertainty Quantification in Multi-Model Ensembles. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.707.

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Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.
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14

Ehresmann, Andrée. Applications of Categories to Biology and Cognition. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198748991.003.0015.

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Mathematical models used in biology are generally adapted from physics and relate to specific local processes. Category theory helps developing global dynamic models account for the main specificities of living systems: (i) The system is evolutionary, with a tangled hierarchy of interacting components, which change over time. (ii) It develops a robust and flexible memory up to the emergence of components and processes of increasing complexity. (iii) It has a multi-agent, multi-temporality, self-organization. This chapter presents such a model, the Memory Evolutive Systems, which in particular characterizes the property at the root of emergence and flexibility. A main application is the model MENS for a neurocognitive system which proposes a physically based “theory of mind”, up to the emergence of higher cognitive processes such as consciousness, anticipation, and creativity.
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15

Bris, Claude Le. Systèmes multi-èchelles: Modélisation et simulation (Mathématiques et Applications). Springer, 2005.

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16

Fuzzi, Sandro, i Dietmar Wagenbach. Cloud Multi-Phase Processes and High Alpine Air and Snow Chemistry: Ground-Based Cloud Experiments and Pollutant Deposition in the High Alps. Springer London, Limited, 2012.

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17

Fuzzi, Sandro, i Dietmar Wagenbach. Cloud Multi-Phase Processes and High Alpine Air and Snow Chemistry: Ground-Based Cloud Experiments and Pollutant Deposition in the High Alps. Springer Berlin / Heidelberg, 2012.

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18

(Editor), Sandro Fuzzi, i Dietmar Wagenbach (Editor), red. Cloud Multi-phase Processes and High Alpine Air and Snow Chemistry: Ground-based Cloud Experiments and Pollutant Deposition in the High Alps (Transport ... of Pollutants in the Troposphere). Springer, 1997.

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