Relevant bibliographies by topics / Multi-Model ensembles

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Academic literature on the topic 'Multi-Model ensembles'

Author: Grafiati
Published: 22 February 2025

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Journal articles on the topic "Multi-Model ensembles"

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Solazzo, E., A. Riccio, I. Kioutsioukis, and S. Galmarini. "Pauci ex tanto numero: reduce redundancy in multi-model ensembles." Atmospheric Chemistry and Physics 13, no. 16 (August 22, 2013): 8315–33. http://dx.doi.org/10.5194/acp-13-8315-2013.

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Abstract. We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date, no attempts in this direction have been documented within the air quality (AQ) community despite the extensive use of ensembles in this field. Common biases and redundancy are the two issues directly deriving from lack of independence, undermining the significance of a multi-model ensemble, and are the subject of this study. Shared, dependant biases among models do not cancel out but will instead determine a biased ensemble. Redundancy derives from having too large a portion of common variance among the members of the ensemble, producing overconfidence in the predictions and underestimation of the uncertainty. The two issues of common biases and redundancy are analysed in detail using the AQMEII ensemble of AQ model results for four air pollutants in two European regions. We show that models share large portions of bias and variance, extending well beyond those induced by common inputs. We make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble with the advantage of being poorly correlated. Selecting the members for generating skilful, non-redundant ensembles from such subsets proved, however, non-trivial. We propose and discuss various methods of member selection and rate the ensemble performance they produce. In most cases, the full ensemble is outscored by the reduced ones. We conclude that, although independence of outputs may not always guarantee enhancement of scores (but this depends upon the skill being investigated), we discourage selecting the members of the ensemble simply on the basis of scores; that is, independence and skills need to be considered disjointly.
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Kioutsioukis, I., and S. Galmarini. "<i>De praeceptis ferendis</i>: good practice in multi-model ensembles." Atmospheric Chemistry and Physics 14, no. 21 (November 11, 2014): 11791–815. http://dx.doi.org/10.5194/acp-14-11791-2014.

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Abstract. Ensembles of air quality models have been formally and empirically shown to outperform single models in many cases. Evidence suggests that ensemble error is reduced when the members form a diverse and accurate ensemble. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to provide better predictions. Theoretical aspects like the bias–variance–covariance decomposition and the accuracy–diversity decomposition are linked together and support the importance of creating ensemble that incorporates both these elements. Hence, the common practice of unconditional averaging of models without prior manipulation limits the advantages of ensemble averaging. We demonstrate the importance of ensemble accuracy and diversity through an inter-comparison of ensemble products for which a sound mathematical framework exists, and provide specific recommendations for model selection and weighting for multi-model ensembles. The sophisticated ensemble averaging techniques, following proper training, were shown to have higher skill across all distribution bins compared to solely ensemble averaging forecasts.
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Beusch, Lea, Lukas Gudmundsson, and Sonia I. Seneviratne. "Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land." Earth System Dynamics 11, no. 1 (February 17, 2020): 139–59. http://dx.doi.org/10.5194/esd-11-139-2020.

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Abstract. Earth system models (ESMs) are invaluable tools to study the climate system's response to specific greenhouse gas emission pathways. Large single-model initial-condition and multi-model ensembles are used to investigate the range of possible responses and serve as input to climate impact and integrated assessment models. Thereby, climate signal uncertainty is propagated along the uncertainty chain and its effect on interactions between humans and the Earth system can be quantified. However, generating both single-model initial-condition and multi-model ensembles is computationally expensive. In this study, we assess the feasibility of geographically explicit climate model emulation, i.e., of statistically producing large ensembles of land temperature field time series that closely resemble ESM runs at a negligible computational cost. For this purpose, we develop a modular emulation framework which consists of (i) a global mean temperature module, (ii) a local temperature response module, and (iii) a local residual temperature variability module. Based on this framework, MESMER, a Modular Earth System Model Emulator with spatially Resolved output, is built. We first show that to successfully mimic single-model initial-condition ensembles of yearly temperature from 1870 to 2100 on grid-point to regional scales with MESMER, it is sufficient to train on a single ESM run, but separate emulators need to be calibrated for individual ESMs given fundamental inter-model differences. We then emulate 40 climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to create a “superensemble”, i.e., a large ensemble which closely resembles a multi-model initial-condition ensemble. The thereby emerging ESM-specific emulator parameters provide essential insights on inter-model differences across a broad range of scales and characterize core properties of each ESM. Our results highlight that, for temperature at the spatiotemporal scales considered here, it is likely more advantageous to invest computational resources into generating multi-model ensembles rather than large single-model initial-condition ensembles. Such multi-model ensembles can be extended to superensembles with emulators like the one presented here.
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Kioutsioukis, I., and S. Galmarini. "<i>De praeceptis ferendis</i>: good practice in multi-model ensembles." Atmospheric Chemistry and Physics Discussions 14, no. 11 (June 17, 2014): 15803–65. http://dx.doi.org/10.5194/acpd-14-15803-2014.

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Abstract. Ensembles of air quality models have been formally and empirically shown to outperform single models in many cases. Evidence suggests that ensemble error is reduced when the members form a diverse and accurate ensemble. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to provide better predictions. There exists a trade-off between diversity and accuracy for which one cannot be gained without expenses of the other. Theoretical aspects like the bias-variance-covariance decomposition and the accuracy-diversity decomposition are linked together and support the importance of creating ensemble that incorporates both the elements. Hence, the common practice of unconditional averaging of models without prior manipulation limits the advantages of ensemble averaging. We demonstrate the importance of ensemble accuracy and diversity through an inter-comparison of ensemble products for which a sound mathematical framework exists, and provide specific recommendations for model selection and weighting for multi model ensembles. To this end we have devised statistical tools that can be used for diagnostic evaluation of ensemble modelling products, complementing existing operational methods.
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Figueiredo, Rui, Kai Schröter, Alexander Weiss-Motz, Mario L. V. Martina, and Heidi Kreibich. "Multi-model ensembles for assessment of flood losses and associated uncertainty." Natural Hazards and Earth System Sciences 18, no. 5 (May 3, 2018): 1297–314. http://dx.doi.org/10.5194/nhess-18-1297-2018.

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Abstract. Flood loss modelling is a crucial part of risk assessments. However, it is subject to large uncertainty that is often neglected. Most models available in the literature are deterministic, providing only single point estimates of flood loss, and large disparities tend to exist among them. Adopting any one such model in a risk assessment context is likely to lead to inaccurate loss estimates and sub-optimal decision-making. In this paper, we propose the use of multi-model ensembles to address these issues. This approach, which has been applied successfully in other scientific fields, is based on the combination of different model outputs with the aim of improving the skill and usefulness of predictions. We first propose a model rating framework to support ensemble construction, based on a probability tree of model properties, which establishes relative degrees of belief between candidate models. Using 20 flood loss models in two test cases, we then construct numerous multi-model ensembles, based both on the rating framework and on a stochastic method, differing in terms of participating members, ensemble size and model weights. We evaluate the performance of ensemble means, as well as their probabilistic skill and reliability. Our results demonstrate that well-designed multi-model ensembles represent a pragmatic approach to consistently obtain more accurate flood loss estimates and reliable probability distributions of model uncertainty.
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Shen, Zhiqiang, Zhankui He, and Xiangyang Xue. "MEAL: Multi-Model Ensemble via Adversarial Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4886–93. http://dx.doi.org/10.1609/aaai.v33i01.33014886.

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Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortunately, the space required to store these many networks, and the time required to execute them at test-time, prohibits their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously. The proposed ensemble method (MEAL) of transferring distilled knowledge with adversarial learning exhibits three important advantages: (1) the student network that learns the distilled knowledge with discriminators is optimized better than the original model; (2) fast inference is realized by a single forward pass, while the performance is even better than traditional ensembles from multi-original models; (3) the student network can learn the distilled knowledge from a teacher model that has arbitrary structures. Extensive experiments on CIFAR-10/100, SVHN and ImageNet datasets demonstrate the effectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEAL achieves top-1/5 21.79%/5.99% val error, which outperforms the original model by 2.06%/1.14%.
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Lee, Kang, Joo, Kim, Kim, and Lee. "Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique." Water 11, no. 4 (April 23, 2019): 850. http://dx.doi.org/10.3390/w11040850.

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The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808–3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models.
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Abdillah, Abid Famasya, Cornelius Bagus Purnama Putra, Apriantoni Apriantoni, Safitri Juanita, and Diana Purwitasari. "Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (April 26, 2022): 42–50. http://dx.doi.org/10.20473/jisebi.8.1.42-50.

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Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making. Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%. Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions. Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering
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Merrifield, Anna Louise, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and Reto Knutti. "An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles." Earth System Dynamics 11, no. 3 (September 16, 2020): 807–34. http://dx.doi.org/10.5194/esd-11-807-2020.

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Abstract. Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single-model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs allow for the quantification of internal variability, a non-negligible component of uncertainty on regional scales, but may also serve to inappropriately narrow uncertainty by giving a single model many additional votes. In advance of the mixed multi-model, the SMILE Coupled Model Intercomparison version 6 (CMIP6) ensemble, we investigate weighting approaches to incorporate 50 members of the Community Earth System Model (CESM1.2.2-LE), 50 members of the Canadian Earth System Model (CanESM2-LE), and 100 members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble. The weights assigned are based on ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) predictors are used to determine the weights, and relationships between present and future predictor behavior are discussed. The estimated residual thermodynamic trend is proposed as an alternative predictor to replace 50-year regional SAT trends, which are more susceptible to internal variability. Uncertainty in estimates of northern European winter and Mediterranean summer end-of-century warming is assessed in a CMIP5 and a combined SMILE–CMIP5 multi-model ensemble. Five different weighting strategies to account for the mix of initial condition (IC) ensemble members and individually represented models within the multi-model ensemble are considered. Allowing all multi-model ensemble members to receive either equal weight or solely a performance weight (based on the root mean square error (RMSE) between members and observations over nine predictors) is shown to lead to uncertainty estimates that are dominated by the presence of SMILEs. A more suitable approach includes a dependence assumption, scaling either by 1∕N, the number of constituents representing a “model”, or by the same RMSE distance metric used to define model performance. SMILE contributions to the weighted ensemble are smallest (<10 %) when a model is defined as an IC ensemble and increase slightly (<20 %) when the definition of a model expands to include members from the same institution and/or development stream. SMILE contributions increase further when dependence is defined by RMSE (over nine predictors) amongst members because RMSEs between SMILE members can be as large as RMSEs between SMILE members and other models. We find that an alternative RMSE distance metric, derived from global SAT and hemispheric SLP climatology, is able to better identify IC members in general and SMILE members in particular as members of the same model. Further, more subtle dependencies associated with resolution differences and component similarities are also identified by the global predictor set.
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Wilkins, Andrew, Aaron Johnson, Xuguang Wang, Nicholas A. Gasperoni, and Yongming Wang. "Multi-Scale Object-Based Probabilistic Forecast Evaluation of WRF-Based CAM Ensemble Configurations." Atmosphere 12, no. 12 (December 6, 2021): 1630. http://dx.doi.org/10.3390/atmos12121630.

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Convection-allowing model (CAM) ensembles contain a distinctive ability to predict convective initiation location, mode, and morphology. Previous studies on CAM ensemble verification have primarily used neighborhood-based methods. A recently introduced object-based probabilistic (OBPROB) framework provides an alternative and novel framework in which to re-evaluate aspects of optimal CAM ensemble design with an emphasis on ensemble storm mode and morphology prediction. Herein, we adopt and extend the OBPROB method in conjunction with a traditional neighborhood-based method to evaluate forecasts of four differently configured 10-member CAM ensembles. The configurations include two single-model/single-physics, a single-model/multi-physics, and a multi-model/multi-physics configuration. Both OBPROB and neighborhood frameworks show that ensembles with more diverse member-to-member designs improve probabilistic forecasts over single-model/single-physics designs through greater sampling of different aspects of forecast uncertainties. Individual case studies are evaluated to reveal the distinct forecast features responsible for the systematic results identified from the different frameworks. Neighborhood verification, even at high reflectivity thresholds, is primarily impacted by mesoscale locations of convective and stratiform precipitation across scales. In contrast, the OBPROB verification explicitly focuses on convective precipitation only and is sensitive to the morphology of similarly located storms.
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Dissertations / Theses on the topic "Multi-Model ensembles"

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Sessford, Patrick Denis. "Quantifying sources of variation in multi-model ensembles : a process-based approach." Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/18121.

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The representation of physical processes by a climate model depends on its structure, numerical schemes, physical parameterizations and resolution, with initial conditions and future emission scenarios further affecting the output. The extent to which climate models agree is therefore of great interest, often with greater confidence in robust results across models. This has led to climate model output being analysed as ensembles rather than in isolation, and quantifying the sources of variation across these ensembles is the aim of many recent studies. Statistical attempts to do this include the use of variants of the mixed-effects analysis of variance or covariance (mixed-effects ANOVA/ANCOVA). This work usually focuses on identifying variation in a variable of interest that is due to differences in model structure, carbon emissions scenario, etc. Quantifying such variation is important in determining where models agree or disagree, but further statistical approaches can be used to diagnose the reasons behind the agreements and disagreements by representing the physical processes within the climate models. A process-based approach is presented that uses simulation with statistical models to perform a global sensitivity analysis and quantify the sources of variation in multi-model ensembles. This approach is a general framework that can be used with any generalised linear mixed model (GLMM), which makes it applicable to use with statistical models designed to represent (sometimes complex) physical relationships within different climate models. The method decomposes the variation in the response variable into variation due to 1) temporal variation in the driving variables, 2) variation across ensemble members in the distributions of the driving variables, 3) variation across ensemble members in the relationship between the response and the driving variables, and 4) variation unexplained by the driving variables. The method is used to quantify the extent to which, and diagnose why, precipitation varies across and within the members of two different climate model ensembles on various different spatial and temporal scales. Change in temperature in response to increased CO2 is related to change in global-mean annual-mean precipitation in a multi-model ensemble of general circulation models (GCMs). A total of 46% of the variation in the change in precipitation in the ensemble is found to be due to the differences between the GCMs, largely because the distribution of the changes in temperature varies greatly across different GCMs. The total variation in the annual-mean change in precipitation that is due to the differences between the GCMs depends on the area over which the precipitation is averaged, and can be as high as 63%. The second climate model ensemble is a perturbed physics ensemble using a regional climate model (RCM). This ensemble is used for three different applications. Firstly, by using lapse rate, saturation specific humidity and relative humidity as drivers of daily-total summer convective precipitation at the grid-point level over southern Britain, up to 8% of the variation in the convective precipitation is found to be due to the uncertainty in RCM parameters. This is largely because given atmospheric conditions lead to different rates of precipitation in different ensemble members. This could not be detected by analysing only the variation across the ensemble members in mean precipitation rate (precipitation bias). Secondly, summer-total precipitation at the grid-point level over the British Isles is used to show how the values of the RCM parameters can be incorporated into a GLMM to quantify the variation in precipitation due to perturbing each individual RCM parameter. Substantial spatial variation is found in the effect on precipitation of perturbing different RCM parameters. Thirdly, the method is extended to focus on extreme events, and the simulation of extreme winter pentad (five-day mean) precipitation events averaged over the British Isles is found to be robust to the uncertainty in RCM parameters.
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Sansom, Philip George. "Statistical methods for quantifying uncertainty in climate projections from ensembles of climate models." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15292.

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Appropriate and defensible statistical frameworks are required in order to make credible inferences about future climate based on projections derived from multiple climate models. It is shown that a two-way analysis of variance framework can be used to estimate the response of the actual climate, if all the climate models in an ensemble simulate the same response. The maximum likelihood estimate of the expected response provides a set of weights for combining projections from multiple climate models. Statistical F tests are used to show that the differences between the climate response of the North Atlantic storm track simulated by a large ensemble of climate models cannot be distinguished from internal variability. When climate models simulate different responses, the differences between the re- sponses represent an additional source of uncertainty. Projections simulated by climate models that share common components cannot be considered independent. Ensemble thinning is advocated in order to obtain a subset of climate models whose outputs are judged to be exchangeable and can be modelled as a random sample. It is shown that the agreement between models on the climate response in the North Atlantic storm track is overestimated due to model dependence. Correlations between the climate responses and historical climates simulated by cli- mate models can be used to constrain projections of future climate. It is shown that the estimate of any such emergent relationship will be biased, if internal variability is large compared to the model uncertainty about the historical climate. A Bayesian hierarchical framework is proposed that is able to separate model uncertainty from internal variability, and to estimate emergent constraints without bias. Conditional cross-validation is used to show that an apparent emergent relationship in the North Atlantic storm track is not robust. The uncertain relationship between an ensemble of climate models and the actual climate can be represented by a random discrepancy. It is shown that identical inferences are obtained whether the climate models are treated as predictors for the actual climate or vice versa, provided that the discrepancy is assumed to be sym- metric. Emergent relationships are reinterpreted as constraints on the discrepancy between the expected response of the ensemble and the actual climate response, onditional on observations of the recent climate. A simple method is proposed for estimating observation uncertainty from reanalysis data. It is estimated that natural variability accounts for 30-45% of the spread in projections of the climate response in the North Atlantic storm track.
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Vogt, Linus. "The role of the upper ocean for global ocean heat uptake and climate." Electronic Thesis or Diss., Sorbonne université, 2024. https://theses.hal.science/tel-04951110.

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Le climat terrestre connaît actuellement des changements rapides et généralisés. Les activités humaines depuis l'ère industrielle, en particulier les émissions de CO2 dans l'atmosphère dues à la combustion de combustibles fossiles, ont intensifié l'effet de serre. Cela a entraîné une augmentation de la température moyenne de l'air à la surface du globe de 1.1°C en 2011-2020 par rapport à 1850-1900. Une autre conséquence majeure est le réchauffement des océans mondiaux, qui ont absorbé plus de 90% de l'énergie excédentaire accumulée dans le système climatique en raison de l'augmentation du forçage radiatif. L'absorption de chaleur par l'océan mondial (OHU) est un processus climatique clé qui joue un double rôle dans le changement climatique d'origine anthropique. D'une part, l'OHU constitue en soi une mesure clé du changement climatique, qui est directement associée à des impacts négatifs tels que l'élévation du niveau de la mer et l'augmentation de la fréquence des événements extrêmes dans l'océan. D'autre part, l'OHU fournit un service climatique essentiel en épargnant l'atmosphère de grandes quantités de chaleur, sans lequel le réchauffement atmosphérique serait bien plus marqué que celui que nous observons actuellement. Malgré leur importance, de nombreux processus physiques qui contrôlent l'OHU restent mal compris, même dans les modèles climatiques numériques utilisés dans les évaluations internationales du changement climatique. Dans cette thèse, nous avançons sur ce problème en nous appuyant sur des simulations climatiques issues de modèles participant au Projet d'intercomparaison des modèles couplés (CMIP). Dans une première étude, nous produisons des estimations améliorées de l'OHU global d'ici à la fin du XXIe siècle en identifiant une relation émergente dans un ensemble de modèles CMIP, qui relie l'état climatique présent de l'hémisphère sud à l'OHU futur. En combinant cette relation avec des données d'observation, nous obtenons des projections mieux contraintes qui montrent que l'OHU futur pourrait être plus important qu'estimé précédemment. Dans une deuxième étude, nous clarifions les processus à l'origine de l'efficacité d'absorption de la chaleur océanique (OHUE), qui quantifie la quantité d'OHU par degré de réchauffement de la surface terrestre. Nous réconcilions plusieurs tentatives antérieures d'explication des facteurs influençant l'OHUE, et montrons que la stratification de l'océan Austral supérieur est une propriété clé qui contrôle l'OHUE dans les modèles climatiques CMIP. Enfin, nous présentons une analyse exploratoire combinant les approches de ces deux études, et menons une analyse statistique des simulations d'un grand ensemble multi-modèle dans le but de contraindre l'OHUE. Au-delà de ces résultats concrets concernant l'OHU global, nous discutons également de certaines questions méthodologiques liées à l'interprétation des incertitudes découlant des ensembles multi-modèles de manière plus générale
The Earth's climate is currently undergoing rapid and widespread changes. Human activities in the industrial era, in particular the emission of CO2 into the atmosphere through the burning of fossil fuels, have led to an enhanced greenhouse effect which has caused an increase in the global average surface air temperature of 1.1°C in 2011-2020 relative to 1850-1900. A further consequence is the warming of the global ocean: it has absorbed over 90% of the excess energy stored in the Earth system due to the increased radiative forcing. This global ocean heat uptake (OHU) is a critical climate process and plays a dual role for anthropogenic climate change. On the one hand, OHU is a measure of the cumulative effects of transient climate change, and scales with negative impacts such as sea level rise and the frequency of oceanic extreme events. On the other hand, OHU provides a crucial service by shielding the atmosphere from large amounts of heat that would otherwise cause much greater global warming than currently observed. Despite their importance, many of the physical processes controlling OHU are still poorly understood, including in state-of-the-art numerical climate models used for international climate change assessments. In this thesis, we address this problem using climate simulations of models participating in the Coupled Model Intercomparison Project (CMIP). In a first study, we provide improved future projections of global OHU by the end of the 21st century by identifying an emergent relationship across an ensemble of CMIP models linking the simulated baseline climate state of the Southern Hemisphere to future global OHU. By combining this relationship with observational data, we obtain constrained projections showing that future OHU is likely larger than previously thought. In a second study, we clarify the processes involved in setting the ocean heat uptake efficiency (OHUE) which quantifies the amount of OHU per degree of global surface warming. We reconcile a number of previous attempts at explaining controls on OHUE, and show that the upper ocean stratification in the Southern Ocean is a key property setting its value in CMIP climate models. Last, we present an exploratory analysis combining the approaches of these two studies, and perform a statistical analysis of simulations from a large multi-model ensemble with the goal of constraining OHUE. Beyond these concrete results concerning global OHU, we also discuss some of the methodological issues related to the interpretation of uncertainties arising from multi-model ensembles more generally
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Tran, Ngo Quoc Huy. "Planification de mouvement pour les systèmes dynamiques multi-agents dans un environnement variable." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT099.

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Cette thèse propose des solutions de commande basées sur la planification optimale de trajectoires pour des systèmes dynamiques multi-agents fonctionnant dans un environnement variable (avec obstacles statiques ou mobiles et des perturbations variables dans le temps).Cette planification de trajectoires repose sur l'utilisation combinée de la théorie des ensembles (en particulier des ensembles convexes bornés), de la commande prédictive non-linéaire (NMPC), du calcul de champs de potentiel et des méthodes basées sur des graphes. Elle se base sur la construction de champs de potentiel répulsifs associés à des fonctions de barrière marche-arrêt (on-off barrier functions) qui décrivent et activent ou désactivent les trajectoires libres (sans collision) calculées au préalable par une commande de type NMPC distribuée. Ces constructions sont ensuite utilisées pour maintenir la connectivité dans le groupe d'agents, tout en assurant le suivi du chemin pré-généré. En outre, un observateur pour l'estimation de perturbations non linéaires est intégré dans le schéma de commande afin de les rejeter.Les résultats théoriques obtenus sont validés en simulation, par des comparaisons avec des approches utilisant la programmation mixte en nombres entiers, à l'aide de données numériques réelles provenant d'une plateforme de navigation sécurisée pour les véhicules de surface non habités dans le fjord de Trondheim (Norvège)
This thesis proposes optimization-based control solutions for the motion planning of multi-agent dynamical systems operating in a variable environment (with static/mobile obstacles and time-varying environmental disturbances).Collision-free paths are planned for the agents through the combined use of set theory (particularly, bounded convex sets), non(-linear) Model Predictive Control (MPC), Potential Field (PF) and graph-based methods. The contributions build on the proposal of repulsive potential field constructions together with on-off barrier functions which describe and, respectively, activate/deactivate the collision-free conditions introduced in a distributed NMPC framework. These constructions are further used for connectivity maintenance conditions among the group of agents while ensuring the tracking of the a priori generated path. Furthermore, a nonlinear disturbance observer is integrated within the control scheme for environmental disturbance rejection.Finally, the results are validated in simulation through comparisons with mixed-integer approaches and over a benchmark for the safe navigation of Unmanned Surface Vehicles (USVs) in the Trondheim fjord, Norway, using real numerical data
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Körner, Stephan [Verfasser], Eike [Akademischer Betreuer] Stumpf, and Ch [Akademischer Betreuer] Breitsamter. "Multi-Model Ensemble Wake Vortex Prediction / Stephan Körner ; Eike Stumpf, Ch. Breitsamter." Aachen : Universitätsbibliothek der RWTH Aachen, 2017. http://d-nb.info/116245122X/34.

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Ben, Houria Zeineb. "Optimisation de la gestion du service de maintenance biomédicale." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES057/document.

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Le milieu hospitalier est un monde à la fois sensible et complexe, sensible parce que la vie humaine est en jeu et complexe parce que les équipements médicaux augmentent en nombre et en complexité technique. Ainsi, afin de préserver le bon état de fonctionnement de ces équipements et à un niveau élevé de disponibilité, leur entretien est devenu l'une des préoccupations majeures des responsables de l’hôpital. L’objectif de cette thèse est de proposer, aux responsables de maintenance biomédicale dans les établissements de soins, des outils d’aide à la décision qui permettent une meilleure maitrise des coûts. Ceci en assurant la sécurité des patients et des utilisateurs et en maintenant des performances optimales de l’ensemble des équipements médicaux. Tout d’abord, une heuristique a été proposée pour le choix de l’internalisation ou de l’externalisation de la maintenance et pour la sélection du contrat adéquat. La sélection du contrat est basée sur un ensemble de critères tout en considérant la contrainte du budget disponible. Ensuite, afin d’améliorer la procédure proposée, nous avons proposé des outils d’aide à la décision multicritère pour le choix adéquat d’une stratégie de maintenance. Pour l’étude de la criticité des équipements médicaux et le choix de la maintenance, sept critères ont été étudiés en proposant un couplage de l’approche AHP « Analytical Hierarchy Process » à la technique TOPSIS « Technique for Order Performance by Similarity to Ideal Solution ». Comme les experts du service de maintenance présentaient une certaine incertitude dans leurs jugements, nous avons intégré l’évaluation linguistique floue dans l’étude de la criticité des équipements et dans la sélection de la stratégie de maintenance (Fuzzy AHP couplée avec Fuzzy TOPSIS). Un modèle mathématique MILP a été développé pour la définition des limites de la criticité afin de caractériser les trois stratégies de maintenance. Le bon choix de ces limites permet d’optimiser le coût de la maintenance en respectant le budget disponible. Enfin, un deuxième modèle mathématique MILP a été développé en se basant sur l’heuristique proposée. Ce modèle permet de sélectionner pour chaque équipement, la stratégie de maintenance, internaliser ou externaliser la maintenance et le type du contrat tout en considérant le budget disponible et la charge/capacité du service maintenance
The hospital is a world that is both sensitive and complex, sensitive because the human life is involved and complex because medical facilities are growing in number and in technical complexity. Then, the problem of the medical equipment maintenance in order to keep them in safe, reliable and with high level of availability has become a major preoccupation of the hospital. The objective of this thesis is to provide tools to help the biomedical maintenance service of the hospital to make decisions that allow a better control of costs, while ensuring patient and user safety and maintaining optimal performance of medical equipment. First, a heuristic has been proposed for the choice of internalization or outsourcing maintenance and for the selection of the appropriate contract. The selection of the contract is based on a set of criteria while considering the available budget constraint. Then, to improve the proposed procedure, we proposed multi-criteria decision-making tools to select the appropriate maintenance strategies. Seven criteria have been designed to study the criticality of medical equipment and the choice of maintenance by providing a coupling of the AHP approach "Analytical Hierarchy Process" with TOPSIS technique "Technique for Order Performance by Similarity to Ideal Solution." As the expert judgments of the maintenance department presented some uncertainty, we integrated the fuzzy language assessment of the criticality of the equipment and the selection of the maintenance strategy (Fuzzy AHP coupled with Fuzzy TOPSIS). A mixed integer linear programming model (MILP) was developed to define thresholds of criticality to characterize the three maintenance strategies. According to these thresholds, maintenance cost can be optimized within the available budget. Finally, a second mixed integer linear programming model (MILP) was developed based on the proposed heuristic. This model allows selecting for each equipment, the maintenance strategy, the internalization or the outsourcing of the maintenance and the type of contract while considering the available budget and the workload / capacity of the maintenance department
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Elvidge, Sean. "On the use of multi-model ensemble techniques for ionospheric and thermospheric characterisation." Thesis, University of Birmingham, 2014. http://etheses.bham.ac.uk//id/eprint/5526/.

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Space weather can have a negative impact on a number of radio frequency (RF) systems, with mitigation by ionospheric and thermospheric modelling one approach to improving system performance. However, before a model can be adopted operationally its performance must be quantified. Taylor diagrams, which show a model’s standard deviation and correlation, have been extended to further illustrate the model’s bias, standard deviation of error and mean square error in comparison to observational data. By normalising the statistics, multiple parameters can be shown simultaneously for a number of models. Using these modified Taylor diagrams, the first known long term (one month) comparison of three model types – empirical, physics and data assimilation - has been performed. The data assimilation models performed best, offering a statistically significant improvement in performance. One physics model performed sufficiently well that it is a viable background model option in future data assimilation schemes. Finally, multi-model thermospheric ensembles (MMEs) have been constructed from which the thermospheric forecasts exhibited a reduced root mean square error compared to non-ensemble approaches. Using an equally weighted MME the reduction was 55% and using a mean square error weighted approach the reduction was 48%.
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Islam, Syed Ataharul. "Multi-model Ensemble Approach for the Assessment of Climate Change Impacts on Water Resources." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/59630.

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This research investigates the impact of climate change on water resources using a multi-model ensemble approach through rainfall-runoff projection for A2 and B1 emission scenarios of IPCC (AR4) for mid (2046-2065) and late (2081-2100) century in selected catchments of Western Australia. A bias correction method is also developed to correct projected runoff and a framework for extended hydrologic prediction (EHP) system is outlined. The findings are expected to be beneficial for planning future water resources.
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Monteiro, Eric. "Contributions aux méthodes numériques pour traiter les non linéarités et les discontinuités dans les matériaux hétérogènes." Phd thesis, Université Paris-Est, 2010. http://tel.archives-ouvertes.fr/tel-00601050.

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Motivé par l'étude de tissus biologiques, ce travail contribue aux développements d'outils numériques permettant de prédire la réponse mécanique de matériaux hétérogènes non linéaires dans lesquels les énergies d'interfaces deviennent prépondérantes. Ainsi, une méthode d'homogénéisation multi échelle combinée à une technique de réduction de modèle basée sur la décomposition orthogonale aux valeurs propres est proposée dans un cadre thermique et hyperélastique. Les énergies d'interfaces entre les différentes phases des composites sont décrites par un modèle d'interface cohérent et prises en compte numériquement par une approche liant la méthode des éléments finis étendus et la méthode level-set. Une étude de l'étalement d'une cellule vivante entre deux lamelles fixes est ensuite réalisée. Les deux modèles utilisés pour les simulations montrent que l'assemblage cortex d'actine-membrane plasmique ne joue qu'un rôle minime dans la réponse mécanique cellulaire
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Ferrone, Alfonso. "Deterministic and probabilistic verification of multi-model meteorological forecasts on the subseasonal timescale." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11195/.

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In questo studio, un multi-model ensemble è stato implementato e verificato, seguendo una delle priorità di ricerca del Subseasonal to Seasonal Prediction Project (S2S). Una regressione lineare è stata applicata ad un insieme di previsioni di ensemble su date passate, prodotte dai centri di previsione mensile del CNR-ISAC e ECMWF-IFS. Ognuna di queste contiene un membro di controllo e quattro elementi perturbati. Le variabili scelte per l'analisi sono l'altezza geopotenziale a 500 hPa, la temperatura a 850 hPa e la temperatura a 2 metri, la griglia spaziale ha risoluzione 1 ◦ × 1 ◦ lat-lon e sono stati utilizzati gli inverni dal 1990 al 2010. Le rianalisi di ERA-Interim sono utilizzate sia per realizzare la regressione, sia nella validazione dei risultati, mediante stimatori nonprobabilistici come lo scarto quadratico medio (RMSE) e la correlazione delle anomalie. Successivamente, tecniche di Model Output Statistics (MOS) e Direct Model Output (DMO) sono applicate al multi-model ensemble per ottenere previsioni probabilistiche per la media settimanale delle anomalie di temperatura a 2 metri. I metodi MOS utilizzati sono la regressione logistica e la regressione Gaussiana non-omogenea, mentre quelli DMO sono il democratic voting e il Tukey plotting position. Queste tecniche sono applicate anche ai singoli modelli in modo da effettuare confronti basati su stimatori probabilistici, come il ranked probability skill score, il discrete ranked probability skill score e il reliability diagram. Entrambe le tipologie di stimatori mostrano come il multi-model abbia migliori performance rispetto ai singoli modelli. Inoltre, i valori più alti di stimatori probabilistici sono ottenuti usando una regressione logistica sulla sola media di ensemble. Applicando la regressione a dataset di dimensione ridotta, abbiamo realizzato una curva di apprendimento che mostra come un aumento del numero di date nella fase di addestramento non produrrebbe ulteriori miglioramenti.
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Books on the topic "Multi-Model ensembles"

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Ryō, Mizuta, ed. Estimation of the future distribution of sea surface temperature and sea ice using the CMIP3 multi-model ensemble mean =: CMIP3 maruchi moderu ansanburu heikin o riyōshita shōrai no kaimen suion kaihyō bunpu no suitei. Tsukuba-shi: Kishō Kenkyūjo, 2008.

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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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Majumdar, Satya N. Random growth models. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.38.

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This article discusses the connection between a particular class of growth processes and random matrices. It first provides an overview of growth model, focusing on the TASEP (totally asymmetric simple exclusion process) with parallel updating, before explaining how random matrices appear. It then describes multi-matrix models and line ensembles, noting that for curved initial data the spatial statistics for large time t is identical to the family of largest eigenvalues in a Gaussian Unitary Ensemble (GUE multi-matrix model. It also considers the link between the line ensemble and Brownian motion, and whether this persists on Gaussian Orthogonal Ensemble (GOE) matrices by comparing the line ensembles at fixed position for the flat polynuclear growth model (PNG) and at fixed time for GOE Brownian motions. Finally, it examines (directed) last passage percolation and random tiling in relation to growth models.
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Burda, Zdzislaw, and Jerzy Jurkiewicz. Phase transitions. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.14.

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This article considers phase transitions in matrix models that are invariant under a symmetry group as well as those that occur in some matrix ensembles with preferred basis, like the Anderson transition. It first reviews the results for the simplest model with a nontrivial set of phases, the one-matrix Hermitian model with polynomial potential. It then presents a view of the several solutions of the saddle point equation. It also describes circular models and their Cayley transform to Hermitian models, along with fixed trace models. A brief overview of models with normal, chiral, Wishart, and rectangular matrices is provided. The article concludes with a discussion of the curious single-ring theorem, the successful use of multi-matrix models in describing phase transitions of classical statistical models on fluctuating two-dimensional surfaces, and the delocalization transition for the Anderson, Hatano-Nelson, and Euclidean random matrix models.
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Book chapters on the topic "Multi-Model ensembles"

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Kioutsioukis, Ioannis, and Stefano Galmarini. "De praeceptis ferendis: Air Quality Multi-model Ensembles." In Springer Proceedings in Complexity, 553–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24478-5_89.

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Solazzo, Efisio, and Stefano Galmarini. "Multi-model Ensembles: How Many Models Do We Need?" In Air Pollution Modeling and its Application XXIII, 505–10. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04379-1_83.

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Galmarini, Stefano, and Slowomir Potempski. "Multi-model Ensembles: Metrics, Indexes, Data Assimilation and All That Jazz." In Air Pollution Modeling and its Application XXI, 419–26. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1359-8_71.

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Hemri, Stephan. "Multi-model Combination and Seamless Prediction." In Handbook of Hydrometeorological Ensemble Forecasting, 1–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-40457-3_19-1.

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Hemri, Stephan. "Multi-model Combination and Seamless Prediction." In Handbook of Hydrometeorological Ensemble Forecasting, 285–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-39925-1_19.

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Dong, Wenjie, Jianbin Huang, Yan Guo, and Fumin Ren. "Comparisons Among Multi-model Ensemble Based on Different Ensemble Methods and Ensemble Member Sizes." In Springer Atmospheric Sciences, 157–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48444-9_3.

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Karaket, Nattapat, Sansanee Auephanwiriyakul, and Nipon Theera-Umpon. "Automobile Parts Localization Using Multi-layer Multi-model Images Classifier Ensemble." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 367–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6757-9_46.

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Mohan Das, Dwarika, R. Singh, A. Kumar, D. R. Mailapalli, A. Mishra, and C. Chatterjee. "A Multi-Model Ensemble Approach for Stream Flow Simulation." In Modeling Methods and Practices in Soil and Water Engineering, 71–102. Oakville, ON ; Waretown, NJ : Apple Academic Press, [2016] |: Apple Academic Press, 2017. http://dx.doi.org/10.1201/b19987-5.

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Chen, Ying, Tiankui Zhang, Rong Huang, Yutao Zhu, and Junhua Hong. "Multi-Model Ensemble-Based Fault Prediction of Telecommunication Networks." In Lecture Notes in Electrical Engineering, 678–86. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8411-4_91.

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Surlikar, Rajas, Akshay Pachore, and Renji Remesan. "Bayesian Model Averaging for Multi-model Ensemble Streamflows of the Godavari Basin." In Water Science and Technology Library, 409–27. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-76532-2_17.

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Conference papers on the topic "Multi-Model ensembles"

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Ponzina, Flavio, Rishikanth Chandrasekaran, Anya Wang, Seiji Minowada, Siddharth Sharma, and Tajana Rosing. "Multi-Model Inference Composition of Hyperdimensional Computing Ensembles." In 2024 IEEE 42nd International Conference on Computer Design (ICCD), 691–98. IEEE, 2024. https://doi.org/10.1109/iccd63220.2024.00111.

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Yu, Jun, Jichao Zhu, Wangyuan Zhu, Zhongpeng Cai, Gongpeng Zhao, Zhihong Wei, Guochen Xie, Zerui Zhang, Qingsong Liu, and Jiaen Liang. "Multi Model Ensemble for Compound Expression Recognition." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 4873–79. IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00491.

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Tian, Chenyu, Rui Lin, and Liang Zhao. "Rod Pumping System Fault Diagnosis Based on Multi-Model Ensemble Method." In 2024 International Conference on Networking, Sensing and Control (ICNSC), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icnsc62968.2024.10760207.

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Xu, Jiayi, Zhijin Qiu, Chen Fan, Bo Wang, Guoqing Song, and Wenkai Ren. "Multi-Model Ensemble Diagnostic Method for Evaporation Duct Considering Physical Property." In 2024 14th International Symposium on Antennas, Propagation and EM Theory (ISAPE), 1–4. IEEE, 2024. https://doi.org/10.1109/isape62431.2024.10841036.

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Patil, Saraswati, Shubhan Punde, Prince Sahani, and Abhinav Salve. "Multi-Model Ensemble Approach for Enhanced Cyberbullying Detection Across Diverse Categories." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), 195–202. IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823191.

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Silva, Anderson, Patrick Valduriez, and Fabio Porto. "Integrating Machine Learning Model Ensembles to the SAVIME Database System." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbbd_estendido.2022.21870.

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The integration of machine learning algorithms into database systems has brought new opportunities in different areas from indexing to query optimization. In this paper, we describe the integration of an approach for the automatic computation of model ensembles to answer a predictive query. We have extended the SAVIME multi-dimensional array DBMS by adding a new function to its query language and implementing the selection and allocation ensemble model dataflow into the query processing component of SAVIME. We show some initial experimental results depicting its performance against a pure Python implementation of the ensemble approach. Interestingly enough the C++ implementation within SAVIME is up to 4 times faster than its competitor.
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Zhang, Rui, Jiming Guo, Hongbo Jiang, Peng Xie, and Chen Wang. "Multi-Task Learning for Location Prediction with Deep Multi-Model Ensembles." In 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2019. http://dx.doi.org/10.1109/hpcc/smartcity/dss.2019.00155.

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Ortt, Derek, Chris Hebert, Bob Weinzapfel, and Devin Eyre. "Development of a Probabilistic Tropical Cyclone Track Uncertainty Cone Using Multi-Model Ensembles." In Offshore Technology Conference. Offshore Technology Conference, 2017. http://dx.doi.org/10.4043/27931-ms.

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Allen, Marshall, Raymundo Arroyave, and Richard Malak. "Deep Ensembles for Modeling Uncertain Phase Constraints In Compositionally Graded Alloy Design." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-89091.

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Abstract Compositionally graded alloys (CGAs) are a specific class of multi-material functionally graded materials (FGMs) that use spatial variations in alloy composition to meet competing performance requirements in at different locations regions of a single part. Directed energy deposition (DED) metal additive technology has enabled the manufacturing of CGAs, but design these alloys remains a challenge. One important challenge is to avoid alloy compositions that result in the formation of deleterious phases during manufacturing. While designers can use CALculation of PHAse Diagram (CALPHAD) models predict the presence of deleterious phases, these calculations tend to be too costly to incorporate directly in a computational design framework. In this work, we apply deep ensembles, or ensembles of deep artificial neural networks (ANNs), to learn a surrogate model of deleterious phase boundaries based on CALPHAD simulations. The learned model is used as a constraint by a path planning algorithm to identify gradient pathway through metal composition space that can be successfully manufactured. We demonstrate the deep ensemble approach in the Fe-Ni-Cr-Ti quaternary system and benchmark it against individual ANNs and a K-nearest neighbors (KNN) approach reported previously. Additionally, we investigate the use of the predicted class probability threshold as a means for understanding surrogate model uncertainty and reasoning about the design space. Lastly, we illustrate how varying the thresholds on constraint probability results in a trade off between manufacturing risk and identifying solutions through narrow passageways.
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Georgiou, Ioannis T. "Pattern Characterization in Acceleration Vector Fields Developed in Complex Beam Structures Subject to an Excitation Protocol by Impulsive Forces." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70504.

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Presented are interesting results concerning aspects of the space-time characteristics of coherence patterns of ensembles of impulsive coupled acceleration signals developed in the complex domain of a physical multi-body flexible structure. A modal hammer is used to systematically pulse-interrogate the structure whereas a state of the art piezoelectric tri-axial accelerometer is used to collect at a point time series samples of coupled dynamics. We find the remarkable fact that collocated ensembles of signals of the acceleration vector field are underlain by strong spatio-temporal coherence which is robust under random experimental error introduced in the impulsive force protocol of interrogation. Coherence is characterized-identified optimally in terms of Proper Orthogonal Decomposition (POD) modes. The POD-identified space-time coherence structures, or patterns, feature an unparalleled classical modal-like characterization of coherence of collocated multi-dimensional information. The identified dominant POD spatio-temporal patterns have the space-time modulation characteristics of classical normal modes of vibration of three-dimensional coupled structural dynamics. Exploiting in full the transient dynamics and being model free, this test and evaluation modal-like identification technique can lead to a reliable certification procedure of multi-body flexible structural systems in critical applications.
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Reports on the topic "Multi-Model ensembles"

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Hansen, James A. Interpreting, Improving, and Augmenting Multi-Model Ensembles. Fort Belvoir, VA: Defense Technical Information Center, February 2006. http://dx.doi.org/10.21236/ada444387.

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Ray, Jaideep, Katherine Regina Cauthen, Sophia Lefantzi, and Lynne Burks. Conditioning multi-model ensembles for disease forecasting. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492995.

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Hansen, James A. Interpreting, Improving, and Augmenting Multi-Model Ensembles. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada629175.

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Pedersen, Gjertrud. Symphonies Reframed. Norges Musikkhøgskole, August 2018. http://dx.doi.org/10.22501/nmh-ar.481294.

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Symphonies Reframed recreates symphonies as chamber music. The project aims to capture the features that are unique for chamber music, at the juncture between the “soloistic small” and the “orchestral large”. A new ensemble model, the “triharmonic ensemble” with 7-9 musicians, has been created to serve this purpose. By choosing this size range, we are looking to facilitate group interplay without the need of a conductor. We also want to facilitate a richness of sound colours by involving piano, strings and winds. The exact combination of instruments is chosen in accordance with the features of the original score. The ensemble setup may take two forms: nonet with piano, wind quartet and string quartet (with double bass) or septet with piano, wind trio and string trio. As a group, these instruments have a rich tonal range with continuous and partly overlapping registers. This paper will illuminate three core questions: What artistic features emerge when changing from large orchestral structures to mid-sized chamber groups? How do the performers reflect on their musical roles in the chamber ensemble? What educational value might the reframing unfold? Since its inception in 2014, the project has evolved to include works with vocal, choral and soloistic parts, as well as sonata literature. Ensembles of students and professors have rehearsed, interpreted and performed our transcriptions of works by Brahms, Schumann and Mozart. We have also carried out interviews and critical discussions with the students, on their experiences of the concrete projects and on their reflections on own learning processes in general. Chamber ensembles and orchestras are exponents of different original repertoire. The difference in artistic output thus hinges upon both ensemble structure and the composition at hand. Symphonies Reframed seeks to enable an assessment of the qualities that are specific to the performing corpus and not beholden to any particular piece of music. Our transcriptions have enabled comparisons and reflections, using original compositions as a reference point. Some of our ensemble musicians have had first-hand experience with performing the original works as well. Others have encountered the works for the first time through our productions. This has enabled a multi-angled approach to the three central themes of our research. This text is produced in 2018.
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Barhak, Jacob. Supplemental Information: The Reference Model is a Multi-Scale Ensemble Model of COVID-19. Outbreak, May 2021. http://dx.doi.org/10.34235/b7eaa32b-1a6b-444f-9848-76f83f5a733c.

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The COVID-19 pandemic has accelerated research worldwide and resulted in a large number of computational models and initiatives. Models were mostly aimed at forecast and resulted in different predictions partially since models were based on different assumptions. In fact the idea that a computational model is just an assumption attempting to explain a phenomenon has not been sufficiently explored. Moreover, the ability to combine models has not been fully realized. The Reference Model for disease progression was performing this task for years for diabetes models and recently started modeling COVID-19. The Reference Model is an ensemble of models that is optimized to fit observed disease phenomenon. The ensemble has the ability to include model components from different sources that compete and cooperate. The recent advance in this model is the ability to include models calculated in different scales, making the model the first known multi-scale ensemble model. This manuscript will review these capabilities and show how multiple models can improve our ability to comprehend the COVID-19 pandemic.
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Ide, Kayo. Multi-Model Ensemble Approaches to Data Assimilation Using the 4D-Local Ensemble Transform Kalman Filter. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada542670.

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Ide, Kayo. Multi-Model Ensemble Approaches to Data Assimilation Using the 4D-Local Ensemble Transform Kalman Filter. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada601440.

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Tribbia, Joseph. NCAR Contribution to A U.S. National Multi-Model Ensemble (NMME) ISI Prediction System. Office of Scientific and Technical Information (OSTI), November 2015. http://dx.doi.org/10.2172/1226920.

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Hinrichs, Claudia, and Judith Hauck. Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution. OceanNets, June 2022. http://dx.doi.org/10.3289/oceannets_d4.4.

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Abstract:
In part one of this deliverable, an ensemble of 14 CMIP6 Earth System Models is evaluated regarding their performance in simulating alkalinity and related parameters. The majority of the models and the multi-model-mean underestimate surface alkalinity compared to climatological observations. Alkalinity biases stemming from the parametrization of calcium carbonate formation and dissolution can be as big as biases stemming from model physics. In part two, we test the sensitivity of parametrizations concerning the carbonate chemistry in the FESOM2.1-REcoM3 and give recommendations for addressing alkalinity biases.
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