• QUESO

  • Referenced in 25 articles [sw10555]
  • research into the uncertainty quantification (UQ) of models and their predictions. It has been designed...
  • UQTk

  • Referenced in 7 articles [sw21917]
  • tools for the quantification of uncertainty in numerical model predictions. Version 3.0.4 offers intrusive ... methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse...
  • I2L-MeshNet

  • Referenced in 2 articles [sw42725]
  • image. In addition, it cannot model the prediction uncertainty, which can make training harder ... lixel (line+pixel) prediction network. The proposed I2L-MeshNet predicts the per-lixel likelihood ... input image and models the prediction uncertainty. We demonstrate the benefit of the image...
  • Virtual library

  • Referenced in 26 articles [sw27598]
  • design and analysis of experiments involving computer models. Under each of the categories found ... both MATLAB and R: Optimization, Emulation/ Prediction, Uncertainty Quantification, Multi Fidelity Simulation, Calibration/ Tuning, Screening...
  • AK-MCS

  • Referenced in 72 articles [sw18303]
  • number of calls to the numerical models. Engineering problems involve more and more complex computer ... Kriging has just started to appear in uncertainty propagation [3] and reliability and studies ... local index of uncertainty on the prediction which can be used in active learning methods...
  • BioBayes

  • Referenced in 26 articles [sw08082]
  • uncertainty about the values of kinetic parameters, about the general structure of the model ... inference provide a consistent framework for modelling and predicting in these uncertain conditions. We present...
  • AQUASIM

  • Referenced in 5 articles [sw25854]
  • values of model parameters (using measured data), and to estimate prediction uncertainty. The computer program...
  • BCM

  • Referenced in 3 articles [sw22361]
  • analyzing the uncertainty in the parameters and the predictions of computational models using Bayesian statistics...
  • Noodles

  • Referenced in 1 article [sw27885]
  • Visualization of Numerical Weather Model Ensemble Uncertainty. Numerical weather prediction ensembles are routinely used ... either slightly perturbed initial conditions or different model parameterizations, or occasionally both. Multi-member ensemble ... interested in understanding the uncertainties associated with numerical weather prediction; specifically variability between the ensemble ... explore new uncertainty visualization methods, the Weather Research and Forecasting (WRF) model was used...
  • PAROC

  • Referenced in 6 articles [sw21878]
  • receding horizon modelling step for model-predictive control (MPC) and reactive scheduling, (iv) a suite ... multi-parametric programming techniques for optimisation under uncertainty, explicit/multi-parametric MPC and state-estimation ... analysed within the original high-fidelity model. The proposed software platform, PAROC, is also introduced...
  • hSDM

  • Referenced in 2 articles [sw24456]
  • species, predicting their probability of occurrence, and assessing uncertainty in the model results...
  • DeepHyper

  • Referenced in 6 articles [sw41119]
  • trial-and-error efforts for developing predictive models. The package performs four key functions: pipeline ... architecture search (DeepHyper/NAS); hyperparameter search (DeepHyper/HPS); ensemble uncertainty quantification (DeepHyper/AutoDEUQ...
  • VBayesLab

  • Referenced in 5 articles [sw41752]
  • natural gradient. Our flexible DFNN models and Bayesian inference approach lead to a regression ... high prediction accuracy, and is able to quantify the prediction uncertainty in a principled...
  • ensAR

  • Referenced in 1 article [sw28376]
  • prediction (NWP) models. These are deterministic simulation models describing the dynamics of the atmosphere ... prediction for future atmospheric states. To account for uncertainty in NWP models it has become ... extension of a recently developed postprocessing model utilizing autoregressive information present in the forecast error ... heteroscedastic model. Furthermore, an additional high-resolution forecast is included into the postprocessing model, yielding...
  • CCL

  • Referenced in 3 articles [sw39608]
  • distributions for a user-defined photometric redshift model. A rigorous validation procedure, based on comparisons ... numerical accuracy for each predicted quantity. As a result, predictions for correlation functions of galaxy ... expected statistical uncertainty of the observables for the models and in the range of scales...
  • mBART

  • Referenced in 2 articles [sw41499]
  • sample predictive performance, and (iii) less post-data uncertainty. While many key aspects ... unconstrained BART model carry over directly to mBART, the introduction of monotonicity constraints necessitates...
  • Quicksilver

  • Referenced in 7 articles [sw38623]
  • model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction ... prediction network which can be sampled during the testing time to calculate uncertainties...
  • proFit

  • Referenced in 1 article [sw39177]
  • model allows to predict (”interpolate”) the output at yet unexplored parameter combinations including uncertainty estimates ... your existing code. Current functionality covers uncertainty quantification via polynomial chaos expansion with chaospy ... backend. Support for response surface / surrogate models via GPflow is under development. The web frontend...
  • statFEM

  • Referenced in 5 articles [sw42054]
  • observation data and model predictions. finite element models. To this end, we propose a novel ... element models. The Bayesian statistical framework is adopted to treat all the uncertainties present...
  • GGMnonreg

  • Referenced in 1 article [sw40478]
  • graphical models, Ising models, and mixed graphical models. The current methods consist of multiple regression ... partial correlations . Parameter uncertainty, predictability, and network replicability