Imperial College London

DrRossellaArcucci

Faculty of EngineeringDepartment of Computing

Research Fellow (Data Analysis)
 
 
 
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Contact

 

r.arcucci Website

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

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34 results found

Ruiz LGB, Pegalajar MC, Arcucci R, Molina-Solana Met al., 2020, A time-series clustering methodology for knowledge extraction in energy consumption data, Expert Systems with Applications, Vol: 160, ISSN: 0957-4174

© 2020 Elsevier Ltd In the Energy Efficiency field, the incorporation of intelligent systems in cities and buildings is motivated by the energy savings and pollution reduction that can be attained. To achieve this goal, energy modelling and a better understanding of how energy is consumed are fundamental factors. As a result, this study proposes a methodology for knowledge acquisition in energy-related data through Time-Series Clustering (TSC) techniques. In our experimentation, we utilize data from the buildings at the University of Granada (Spain) and compare several clustering methods to get the optimum model, in particular, we tested k-Means, k-Medoids, Hierarchical clustering and Gaussian Mixtures; as well as several algorithms to obtain the best grouping, such as PAM, CLARA, and two variants of Lloyd's method, Small and Large. Thus, our methodology can provide non-trivial knowledge from raw energy data. In contrast to previous studies in this field, not only do we propose a clustering methodology to group time series straightforwardly, but we also present an automatic strategy to search and analyse energy periodicity in these series recursively so that we can deepen granularity and extract information at different levels of detail. The results show that k-Medoids with PAM is the best approach in virtually all cases, and the Squared Euclidean distance outperforms the rest of the metrics.

Journal article

Mack J, Arcucci R, Molina-Solana M, Guo YKet al., 2020, Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation, Computer Methods in Applied Mechanics and Engineering, Vol: 372, ISSN: 0045-7825

© 2020 Elsevier B.V. We propose a new ‘Bi-Reduced Space’ approach to solving 3D Variational Data Assimilation using Convolutional Autoencoders. We prove that our approach has the same solution as previous methods but has significantly lower computational complexity; in other words, we reduce the computational cost without affecting the data assimilation accuracy. We tested our proposal with data from a real-world application: a pollution model of a site in Elephant and Castle (London, UK) and found that we could (1) reduce the size of the background covariance matrix representation by O(103), and (2) increase our data assimilation accuracy with respect to existing reduced space methods.

Journal article

Casas CQ, Arcucci R, Wu P, Pain C, Guo Y-Ket al., 2020, A Reduced Order Deep Data Assimilation model, PHYSICA D-NONLINEAR PHENOMENA, Vol: 412, ISSN: 0167-2789

Journal article

Nadler P, Wang S, Arcucci R, Yang X, Guo Yet al., 2020, An epidemiological modelling approach for Covid19 via data assimilation, Publisher: arXiv

The global pandemic of the 2019-nCov requires the evaluation of policyinterventions to mitigate future social and economic costs of quarantinemeasures worldwide. We propose an epidemiological model for forecasting andpolicy evaluation which incorporates new data in real-time through variationaldata assimilation. We analyze and discuss infection rates in China, the US andItaly. In particular, we develop a custom compartmental SIR model fit tovariables related to the epidemic in Chinese cities, named SITR model. Wecompare and discuss model results which conducts updates as new observationsbecome available. A hybrid data assimilation approach is applied to makeresults robust to initial conditions. We use the model to do inference oninfection numbers as well as parameters such as the disease transmissibilityrate or the rate of recovery. The parameterisation of the model is parsimoniousand extendable, allowing for the incorporation of additional data andparameters of interest. This allows for scalability and the extension of themodel to other locations or the adaption of novel data sources.

Working paper

Dur TH, Arcucci R, Mottet L, Molina Solana M, Pain C, Guo Y-Ket al., 2020, Weak Constraint Gaussian Processes for optimal sensor placement, JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 42, ISSN: 1877-7503

Journal article

Wu P, Sun J, Chang X, Zhang W, Arcucci R, Guo Y, Pain CCet al., 2020, Data-driven reduced order model with temporal convolutional neural network, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 360, ISSN: 0045-7825

Journal article

Arcucci R, Casas CQ, Xiao D, Mottet L, Fang F, Wu P, Pain C, Guo YKet al., 2020, A domain decomposition reduced order model with data assimilation (DD-RODA), Pages: 189-198, ISSN: 0927-5452

© 2020 The authors and IOS Press. We present a Domain Decomposition Reduced Order Data Assimilation (DD-RODA) model which combines Non-Intrusive Reduced Order Modelling (NIROM) method with a Data Assimilation (DA) model. The NIROM is defined on a partition of the domain in sub-domains with overlapping regions and the DA is defined on a partition of the domain in sub-domains without overlapping regions. This choice allows to avoid communications among the processes during the Data Assimilation phase. However, during the balance phase, the model exploits the domain decomposition implemented in DD-NIROM which balances the results among the processes exploiting overlapping regions. The model is applied to the pollutant dispersion within an urban environment. Simulations are performed using the open-source, finite-element, fluid dynamics model Fluidity.

Conference paper

Arcucci R, Mottet L, Casas CAQ, Guitton F, Pain C, Guo YKet al., 2020, Adaptive Domain Decomposition for Effective Data Assimilation, Pages: 583-595, ISSN: 0302-9743

© 2020, Springer Nature Switzerland AG. We present a parallel Data Assimilation model based on an Adaptive Domain Decomposition (ADD-DA) coupled with the open-source, finite-element, fluid dynamics model Fluidity. The model we present is defined on a partition of the domain in sub-domains without overlapping regions. This choice allows to avoid communications among the processes during the Data Assimilation phase. However, during the balance phase, the model exploits the domain decomposition implemented in Fluidity which balances the results among the processes exploiting overlapping regions. Also, the model exploits the technology provided by the mesh adaptivity to generate an optimal mesh we name supermesh. The supermesh is the one used in ADD-DA process. We prove that the ADD-DA model provides the same numerical solution of the corresponding sequential DA model. We also show that the ADD approach reduces the execution time even when the implementation is not on a parallel computing environment. Experimental results are provided for pollutant dispersion within an urban environment.

Conference paper

Arcucci R, Moutiq L, Guo YK, 2020, Neural assimilation, Pages: 155-168, ISBN: 9783030504328

© Springer Nature Switzerland AG 2020. We introduce a new neural network for Data Assimilation (DA). DA is the approximation of the true state of some physical system at a given time obtained combining time-distributed observations with a dynamic model in an optimal way. The typical assimilation scheme is made up of two major steps: a prediction and a correction of the prediction by including information provided by observed data. This is the so called prediction-correction cycle. Classical methods for DA include Kalman filter (KF). KF can provide a rich information structure about the solution but it is often complex and time-consuming. In operational forecasting there is insufficient time to restart a run from the beginning with new data. Therefore, data assimilation should enable real-time utilization of data to improve predictions. This mandates the choice of an efficient data assimilation algorithm. Due to this necessity, we introduce, in this paper, the Neural Assimilation (NA), a coupled neural network made of two Recurrent Neural Networks trained on forecasting data and observed data respectively. We prove that the solution of NA is the same of KF. As NA is trained on both forecasting and observed data, after the phase of training NA is used for the prediction without the necessity of a correction given by the observations. This allows to avoid the prediction-correction cycle making the whole process very fast. Experimental results are provided and NA is tested to improve the prediction of oxygen diffusion across the Blood-Brain Barrier (BBB).

Book chapter

Nadler P, Arcucci R, Guo YK, 2020, A scalable approach to econometric inference, Pages: 59-68, ISSN: 0927-5452

© 2020 The authors and IOS Press. We propose a novel approach combining vector autoregressive models and data assimilation to conduct econometric inference for high dimensional problems in cryptocurrency markets. We label this new model TVP-VAR-DA. As the resulting algorithm is computationally very expensive, it mandates the introduction of a problem decomposition and its implementation in a parallel computing environment. We study its scalability and prediction accuracy under various specifications.

Conference paper

Nadler P, Arcucci R, Guo YK, 2019, Data assimilation for parameter estimation in economic modelling, Pages: 649-656

© 2019 IEEE. We propose a data assimilation approach for latent parameter estimation in economic models. We describe a dynamic model of an economic system with latent state variables describing the relationship of economic entities over time as well as a stochastic volatility component. We show and discuss the model's relationship with data assimilation and how it is derived. We apply it to conduct a multivariate analysis of the cryptocurrency ecosystem. Combining these approaches opens a new dimension of analysis to economic modelling. Economics, Multivariate Analysis, Dynamical System, Bitcoin, Data Assimilation.

Conference paper

Aristodemou E, Arcucci R, Mottet L, Robins A, Pain C, Guo Y-Ket al., 2019, Enhancing CFD-LES air pollution prediction accuracy using data assimilation, BUILDING AND ENVIRONMENT, Vol: 165, ISSN: 0360-1323

Journal article

Lim EM, Molina Solana M, Pain C, Guo YK, Arcucci Ret al., 2019, Hybrid data assimilation: An ensemble-variational approach, Pages: 633-640

© 2019 IEEE. Data Assimilation (DA) is a technique used to quantify and manage uncertainty in numerical models by incorporating observations into the model. Variational Data Assimilation (VarDA) accomplishes this by minimising a cost function which weighs the errors in both the numerical results and the observations. However, large-scale domains pose issues with the optimisation and execution of the DA model. In this paper, ensemble methods are explored as a means of sampling the background error at a reduced rank to condition the problem. The impact of ensemble size on the error is evaluated and benchmarked against other preconditioning methods explored in previous work such as using truncated singular value decomposition (TSVD). Localisation is also investigated as a form of reducing the long-range spurious errors in the background error covariance matrix. Both the mean squared error (MSE) and execution time are used as measure of performance. Experimental results for a 3D case for pollutant dispersion within an urban environment are presented with promise for future work using dynamic ensembles and 4D state vectors.

Conference paper

Zhu J, Hu S, Arcucci R, Xu C, Zhu J, Guo Y-Ket al., 2019, Model error correction in data assimilation by integrating neural networks, Big Data Mining and Analytics, Vol: 2, Pages: 83-91, ISSN: 2096-0654

In this paper, we suggest a new methodology which combines Neural Networks (NN) into Data Assimilation (DA). Focusing on the structural model uncertainty, we propose a framework for integration NN with the physical models by DA algorithms, to improve both the assimilation process and the forecasting results. The NNs are iteratively trained as observational data is updated. The main DA models used here are the Kalman filter and the variational approaches. The effectiveness of the proposed algorithm is validated by examples and by a sensitivity study.

Journal article

Arcucci R, Mottet L, Pain C, Guo Y-Ket al., 2019, Optimal reduced space for Variational Data Assimilation, JOURNAL OF COMPUTATIONAL PHYSICS, Vol: 379, Pages: 51-69, ISSN: 0021-9991

Journal article

Arcucci R, McIlwraith D, Guo YK, 2019, Scalable Weak Constraint Gaussian Processes, Pages: 111-125, ISSN: 0302-9743

© 2019, Springer Nature Switzerland AG. A Weak Constraint Gaussian Process (WCGP) model is presented to integrate noisy inputs into the classical Gaussian Process predictive distribution. This follows a Data Assimilation approach i.e. by considering information provided by observed values of a noisy input in a time window. Due to the increased number of states processed from real applications and the time complexity of GP algorithms, the problem mandates a solution in a high performance computing environment. In this paper, parallelism is explored by defining the parallel WCGP model based on domain decomposition. Both a mathematical formulation of the model and a parallel algorithm are provided. We prove that the parallel implementation preserves the accuracy of the sequential one. The algorithm’s scalability is further proved to be where p is the number of processors.

Conference paper

Arcucci R, Pain C, Guo Y-K, 2018, Effective variational data assimilation in air-pollution prediction, Big Data Mining and Analytics, Vol: 1, Pages: 297-307, ISSN: 2096-0654

Numerical simulations are widely used as a predictive tool to better understand complex air flows and pollution transport on the scale of individual buildings, city blocks, and entire cities. To improve prediction for air flows and pollution transport, we propose a Variational Data Assimilation (VarDA) model which assimilates data from sensors into the open-source, finite-element, fluid dynamics model Fluidity. VarDA is based on the minimization of a function which estimates the discrepancy between numerical results and observations assuming that the two sources of information, forecast and observations, have errors that are adequately described by error covariance matrices. The conditioning of the numerical problem is dominated by the condition number of the background error covariance matrix which is ill-conditioned. In this paper, a preconditioned VarDA model is presented, it is based on a reduced background error covariance matrix. The Empirical Orthogonal Functions (EOFs) method is used to alleviate the computational cost and reduce the space dimension. Experimental results are provided assuming observed values provided by sensors from positions mainly located on roofs of buildings.

Journal article

Song J, Fan S, Lin W, Mottet L, Woodward H, Wykes MD, Arcucci R, Xiao D, Debay J-E, ApSimon H, Aristodemou E, Birch D, Carpentieri M, Fang F, Herzog M, Hunt GR, Jones RL, Pain C, Pavlidis D, Robins AG, Short CA, Linden PFet al., 2018, Natural ventilation in cities: the implications of fluid mechanics, BUILDING RESEARCH AND INFORMATION, Vol: 46, Pages: 809-828, ISSN: 0961-3218

Journal article

D'Amore L, Arcucci R, Li Y, Montella R, Moore A, Phillipson L, Toumi Ret al., 2018, Performance Assessment of the Incremental Strong Constraints 4DVAR Algorithm in ROMS, 12th International Conference on Parallel Processing and Applied Mathematics (PPAM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 48-57, ISSN: 0302-9743

Conference paper

Arcucci R, Basciano D, Cilardo A, D'Amore L, Mantovani Fet al., 2018, Energy Analysis of a 4D Variational Data Assimilation Algorithm and Evaluation on ARM-Based HPC Systems, 12th International Conference on Parallel Processing and Applied Mathematics (PPAM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 37-47, ISSN: 0302-9743

Conference paper

Arcucci R, Carracciuolo L, Toumi R, 2018, Toward a preconditioned scalable 3DVAR for assimilating Sea Surface Temperature collected into the Caspian Sea, Journal of Numerical Analysis, Industrial and Applied Mathematics, Vol: 12, Pages: 9-28, ISSN: 1790-8140

© 2018 European Society of Computational Methods in Sciences and Engineering. Data Assimilation (DA) is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. As a crucial point into DA models is the ill conditioning of the covariance matrices involved, it is mandatory to introduce, in a DA software, preconditioning methods. Here we present first results obtained introducing two different preconditioning methods in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea by using the Regional Ocean Modeling System (ROMS) with observations provided by the Group of High resolution sea surface temperature (GHRSST). We present the algorithmic strategies we employ and the numerical issues on data collected in two of the months which present the most significant variability in water temperature: August and March.

Journal article

Arcucci R, D'Amore L, Carracciuolo L, Scotti G, Laccetti Get al., 2017, A Decomposition of the Tikhonov Regularization Functional Oriented to Exploit Hybrid Multilevel Parallelism, INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING, Vol: 45, Pages: 1214-1235, ISSN: 0885-7458

Journal article

Arcucci R, Celestino S, Toumi R, Laccetti Get al., 2017, Toward the S3DVAR data assimilation software for the Caspian Sea, International Conference on Numerical Analysis and Applied Mathematics (ICNAAM), Publisher: AIP Publishing, ISSN: 1551-7616

Data Assimilation (DA) is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. The forecasting model used for producing oceanographic prediction into the Caspian Sea is the Regional Ocean Modeling System (ROMS). Here we propose the computational issues we are facing in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea with observations provided by the Group of High resolution sea surface temperature (GHRSST). We present the algorithmic strategies we employ and the numerical issues on data collected in two of the months which present the most significant variability in water temperature: August and March.

Conference paper

Arcucci R, Marotta U, Murano A, Sorrentino Let al., 2017, Parallel Parity Games: a Multicore Attractor for the Zielonka Recursive Algorithm, International Conference on Computational Science (ICCS), Publisher: ELSEVIER SCIENCE BV, Pages: 525-534, ISSN: 1877-0509

Conference paper

Arcucci R, D'Amore L, Mele V, 2017, Mathematical Approach to the Performance Evaluation of Three Dimensional Variational Data Assimilation, 1st International Conference on Applied Mathematics and Computer Science (ICAMCS), Publisher: AMER INST PHYSICS, ISSN: 0094-243X

Conference paper

Arcucci R, D'Amore L, Toumi R, 2017, Preconditioning of the background error covariance matrix in data assimilation for the Caspian Sea, 1st International Conference on Applied Mathematics and Computer Science (ICAMCS), Publisher: AIP Publishing, ISSN: 1551-7616

Data Assimilation (DA) is an uncertainty quantification technique used for improving numerical forecasted results by incorporating observed data into prediction models. As a crucial point into DA models is the ill conditioning of the covariance matrices involved, it is mandatory to introduce, in a DA software, preconditioning methods. Here we present first studies concerning the introduction of two different preconditioning methods in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea by using the Regional Ocean Modeling System (ROMS) with observations provided by the Group of High resolution sea surface temperature (GHRSST). We also present the algorithmic strategies we employ.

Conference paper

Arcucci R, D'Amore L, Pistoia J, Toumi R, Murli Aet al., 2017, On the variational data assimilation problem solving and sensitivity analysis, JOURNAL OF COMPUTATIONAL PHYSICS, Vol: 335, Pages: 311-326, ISSN: 0021-9991

We consider the Variational Data Assimilation (VarDA) problem in an operational framework, namely, as it results when it is employed for the analysis of temperature and salinity variations of data collected in closed and semi closed seas. We present a computing approach to solve the main computational kernel at the heart of the VarDA problem, which outperforms the technique nowadays employed by the oceanographic operative software. The new approach is obtained by means of Tikhonov regularization. We provide the sensitivity analysis of this approach and we also study its performance in terms of the accuracy gain on the computed solution. We provide validations on two realistic oceanographic data sets.

Journal article

Arcucci R, D'Amore L, Celestino S, Laccetti G, Murli Aet al., 2016, A Scalable Numerical Algorithm for Solving Tikhonov Regularization Problems, 11th International Conference on Parallel Processing and Applied Mathematics (PPAM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 45-54, ISSN: 0302-9743

Conference paper

Arcucci R, D'Amore L, Carracciuolo L, 2015, On the Problem-Decomposition of Scalable 4D-Var Data Assimilation Models, International Conference on High Performance Computing and Simulation (HPCS), Publisher: IEEE, Pages: 589-594

Conference paper

D'Amore L, Arcucci R, Carracciuolo L, Murli Aet al., 2014, A Scalable Approach for Variational Data Assimilation, JOURNAL OF SCIENTIFIC COMPUTING, Vol: 61, Pages: 239-257, ISSN: 0885-7474

Journal article

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