Erratum to “On DFT Molecular Simulation for Non-Adaptive Kernel Approximation” [Advances in Materials Physics and Chemistry Vol. 4 No. 6 (June 2014) 105-115]

The original online version of this article (Randrianarivony, M. (June 2014) On DFT Molecular Simulation for Non-Adaptive Kernel Approximation. Advances in Materials Physics and Chemistry, Vol. 4 No. 6, 105-115. http://dx.doi.org/10.4236/ampc.2014.46013) did not contain any acknowledgment. The author wishes to add the following acknowledgements: Acknowledgements: This work was partially supported by Eurostars Project E!6935 funded by German Federal Ministry of Education and Research.

Erratum to “Multilevel B-Spline Repulsive Energy in Nanomodeling of Graphenes” [Journal of Surface Engineered Materials and Advanced Technology Vol. 4 No. 2 (April 2014) 75-86]

Quantum energies which are used in applications are usually composed of repulsive and attractive terms. The objective of this study is to use an accurate and efficient fitting of the repulsive energy instead of using standard parametrizations. The investigation is based on Density Functional Theory and Tight Binding simulations. Our objective is not only to capture the values of the repulsive terms but also to efficiently reproduce the elastic properties and the forces. The elasticity values determine the rigidity of a material when some traction or load is applied on it. The pair-potential is based on an exponential term corrected by B-spline terms. In order to accelerate the computations, one uses a hierarchical optimization for the B-splines on different levels. Carbon graphenes constitute the configurations used in the simulations. We report on some results to show the efficiency of the B-splines on different levels.

On DFT Molecular Simulation for Non-Adaptive Kernel Approximation

Using accurate quantum energy computations in nanotechnologic applications is usually very computationally intensive. That makes it difficult to apply in subsequent quantum simulation. In this paper, we present some preliminary results pertaining to stochastic methods for alleviating the numerical expense of quantum estimations. The initial information about the quantum energy originates from the Density Functional Theory. The determination of the parameters is performed by using methods stemming from machine learning. We survey the covariance method using marginal likelihood for the statistical simulation. More emphasis is put at the position of equilibrium where the total atomic energy attains its minimum. The originally intensive data can be reproduced efficiently without losing accuracy. A significant acceleration gain is perceived by using the proposed method.

Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic.This report summarizes the rich discussions that occurred during the workshop.The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater monitoring.Significant advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends.The role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic preparedness.Overall,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

An efficient geosciences workflow on multi-core processors and GPUs:a case study for aerosol optical depth retrieval from MODIS satellite data

Quantitative remote sensing retrieval algorithms help understanding the dynamic aspects of Digital Earth.However,the Big Data and complex models in Digital Earth pose grand challenges for computation infrastructures.In this article,taking the aerosol optical depth(AOD)retrieval as a study case,we exploit parallel computing methods for high efficient geophysical parameter retrieval.We present an efficient geocomputation workflow for the AOD calculation from the Moderate Resolution Imaging Spectroradiometer(MODIS)satellite data.According to their individual potential for parallelization,several procedures were adapted and implemented for a successful parallel execution on multicore processors and Graphics Processing Units(GPUs).The benchmarks in this paper validate the high parallel performance of the retrieval workflow with speedups of up to 5.x on a multi-core processor with 8 threads and 43.x on a GPU.To specifically address the time-consuming model retrieval part,hybrid parallel patterns which combine the multicore processor’s and the GPU’s compute power were implemented with static and dynamic workload distributions and evaluated on two systems with different CPU–GPU configurations.It is shown that only the dynamic hybrid implementation leads to a greatly enhanced overall exploitation of the heterogeneous hardware environment in varying circumstances.

Multilevel B-Spline Repulsive Energy in Nanomodeling of Graphenes

Quantum energies which are used in applications are usually composed of repulsive and attractive terms. The objective of this study is to use an accurate and efficient fitting of the repulsive energy instead of using standard parametrizations. The investigation is based on Density Functional Theory and Tight Binding simulations. Our objective is not only to capture the values of the repulsive terms but also to efficiently reproduce the elastic properties and the forces. The elasticity values determine the rigidity of a material when some traction or load is applied on it. The pair-potential is based on an exponential term corrected by B-spline terms. In order to accelerate the computations, one uses a hierarchical optimization for the B-splines on different levels. Carbon graphenes constitute the configurations used in the simulations. We report on some results to show the efficiency of the B-splines on different levels.