Integration of expert knowledge into radial basis function surrogate models

A current application in a collaboration between Chalmers University of Technology and Volvo Group Trucks Technology concerns the global optimization of a complex simulation-based function describing the rolling resistance coefficient of a truck tyre. This function is crucial for the optimization of truck tyres selection considered. The need to explicitly describe and optimize this function provided the main motivation for the research presented in this article. Many optimization algorithms for simulation-based optimization problems use sample points to create a computationally simple surrogate model of the objective function. Typically, not all important characteristics of the complex function (as, e.g., non-negativity)—here referred to as expert knowledge—are automatically inherited by the surrogate model. We demonstrate the integration of several types of expert knowledge into a radial basis function interpolation. The methodology is first illustrated on a simple example function and then applied to a function describing the rolling resistance coefficient of truck tyres. Our numerical results indicate that expert knowledge can be advantageously incorporated and utilized when creating global approximations of unknown functions from sample points

Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space

This paper concerns the solution of a class of mathematical optimization problems with simulation-based objective functions. The decision variables are partitioned into two groups, referred to as variables and parameters, respectively, such that the objective function value is influenced more by the variables than by the parameters. We aim to solve this optimization problem for a large number of parameter settings in a computationally efficient way. The algorithm developed uses surrogate models of the objective function for a selection of parameter settings, for each of which it computes an approximately optimal solution over the domain of the variables. Then, approximate optimal solutions for other parameter settings are computed through a weighting of the surrogate models without requiring additional expensive function evaluations. We have tested the algorithm\u27s performance on a set of global optimization problems differing with respect to both mathematical properties and numbers of variables and parameters. Our results show that it outperforms a\ua0standard and often applied approach based on a surrogate model of the objective function over the complete space of variables and parameters

Attributing the variability of eddy‐covariance CO 2 flux measurements across temporal scales using geostatistical regression for a mixed northern hardwood forest

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95220/1/gbc1702.pd

Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe's terrestrial ecosystems : a review

Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO2, CH4, N2O, H2O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.Peer reviewe

Role of transcriptional regulation in the evolution of plant phenotype: A dynamic systems approach

© 2015 Wiley Periodicals, Inc. A growing body of evidence suggests that alterations in transcriptional regulation of genes involved in modulating development are an important part of phenotypic evolution, and this can be documented among species and within populations. While the effects of differential transcriptional regulation in organismal development have been preferentially studied in animal systems, this phenomenon has also been addressed in plants. In this review, we summarize evidence for cis-regulatory mutations, trans-regulatory changes and epigenetic modifications as molecular events underlying important phenotypic alterations, and thus shaping the evolution of plant development. We postulate that a mechanistic understanding of why such molecular alterations have a key role in development, morphology and evolution will have to rely on dynamic models of complex regulatory networks that consider the concerted action of genetic and nongenetic components, and that also incorporate the restrictions underlying the genotype to phenotype mapping process.CONACyT 180098, 180380, 167705, 152649 and PAPIIT UNAM IN203214-3, IN203113-3, IN203814-3. BFU2012–34821 (MINECO) to C.G. and an institutional grant from Fundación Ramón Aceres to CBMSOPeer Reviewe

Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model

Aim: The mechanisms of plant trait adaptation and acclimation are still poorly understood and, consequently, lack a consistent representation in terrestrial biosphere models (TBMs). Despite the increasing availability of geo‐referenced trait observations, current databases are still insufficient to cover all vegetation types and environmental conditions. In parallel, the growing number of continuous eddy‐covariance observations of energy and CO2 fluxes has enabled modellers to optimize TBMs with these data. Past attempts to optimize TBM parameters mostly focused on model performance, overlooking the ecological properties of ecosystems. The aim of this study was to assess the ecological consistency of optimized trait‐related parameters while improving the model performances for gross primary productivity (GPP) at sites. Location: Worldwide. Time period: 1992–2012. Major taxa studied: Trees and C3 grasses. Methods: We optimized parameters of the ORCHIDEE model against 371 site‐years of GPP estimates from the FLUXNET network, and we looked at global covariation among parameters and with climate. Results: The optimized parameter values were shown to be consistent with leaf‐scale traits, in particular, with well‐known trade‐offs observed at the leaf level, echoing the leaf economic spectrum theory. Results showed a marked sensitivity of trait‐related parameters to local bioclimatic variables and reproduced the observed relationships between traits and climate. Main conclusions: Our approach validates some biological processes implemented in the model and enables us to study ecological properties of vegetation at the canopy level, in addition to some traits that are difficult to observe experimentally. This study stresses the need for: (a) implementing explicit trade‐offs and acclimation processes in TBMs; (b) improving the representation of processes to avoid model‐specific parameterization; and (c) performing systematic measurements of traits at FLUXNET sites in order to gather information on plant ecophysiology and plant diversity, together with micro‐meteorological conditions