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
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 …
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 …
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 …
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 …