Sparse polynomial chaos expansions: Literature survey and benchmark
Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …
Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems
Land surface models (LSMs) are a vital tool for understanding, projecting, and predicting the
dynamics of the land surface and its role within the Earth system, under global change …
dynamics of the land surface and its role within the Earth system, under global change …
A global Fine‐Root Ecology Database to address below‐ground challenges in plant ecology
CM Iversen, ML McCormack, AS Powell… - New …, 2017 - Wiley Online Library
Variation and tradeoffs within and among plant traits are increasingly being harnessed by
empiricists and modelers to understand and predict ecosystem processes under changing …
empiricists and modelers to understand and predict ecosystem processes under changing …
Polynomial-chaos-based Kriging
Computer simulation has become the standard tool in many engineering fields for designing
and optimizing systems, as well as for assessing their reliability. Optimization and …
and optimizing systems, as well as for assessing their reliability. Optimization and …
Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm
Structural damage identification plays a crucial role in structural health monitoring. In this
study, a novelty method for structural damage identification is developed, which employs an …
study, a novelty method for structural damage identification is developed, which employs an …
[HTML][HTML] Second-order reliability methods: a review and comparative study
Second-order reliability methods are commonly used for the computation of reliability,
defined as the probability of satisfying an intended function in the presence of uncertainties …
defined as the probability of satisfying an intended function in the presence of uncertainties …
Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes
Global sensitivity analysis is now established as a powerful approach for determining the
key random input parameters that drive the uncertainty of model output predictions. Yet the …
key random input parameters that drive the uncertainty of model output predictions. Yet the …
Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4. 5 (ED)
RA Fisher, S Muszala, M Verteinstein… - Geoscientific Model …, 2015 - gmd.copernicus.org
We describe an implementation of the Ecosystem Demography (ED) concept in the
Community Land Model. The structure of CLM (ED) and the physiological and structural …
Community Land Model. The structure of CLM (ED) and the physiological and structural …
Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies
Sampling orthogonal polynomial bases via Monte Carlo is of interest for uncertainty
quantification of models with random inputs, using Polynomial Chaos (PC) expansions. It is …
quantification of models with random inputs, using Polynomial Chaos (PC) expansions. It is …
Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence
Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …