Search or filter publications

Filter by type:

Filter by publication type

Filter by year:

to

Results

  • Showing results for:
  • Reset all filters

Search results

  • Journal article
    Cheng S, Chen J, Anastasiou C, Angeli P, Matar OKK, Guo Y-K, Pain CCC, Arcucci Ret al., 2023,

    Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models

    , Journal of Scientific Computing, Vol: 94, Pages: 1-37, ISSN: 0885-7474

    Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve the algorithmic efficiency. In this paper, we develop a system which combines reduced-order surrogate models with a novel data assimilation (DA) technique used to incorporate real-time observations from different physical spaces. We make use of local smooth surrogate functions which link the space of encoded system variables and the one of current observations to perform variational DA with a low computational cost. The new system, named generalised latent assimilation can benefit both the efficiency provided by the reduced-order modelling and the accuracy of data assimilation. A theoretical analysis of the difference between surrogate and original assimilation cost function is also provided in this paper where an upper bound, depending on the size of the local training set, is given. The new approach is tested on a high-dimensional (CFD) application of a two-phase liquid flow with non-linear observation operators that current Latent Assimilation methods can not handle. Numerical results demonstrate that the proposed assimilation approach can significantly improve the reconstruction and prediction accuracy of the deep learning surrogate model which is nearly 1000 times faster than the CFD simulation.
  • Journal article
    Gong H, Cheng S, Chen Z, Li Q, Quilodran-Casas C, Xiao D, Arcucci Ret al., 2022,

    An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics

    , ANNALS OF NUCLEAR ENERGY, Vol: 179, ISSN: 0306-4549
  • Journal article
    Chagot L, Quilodran-Casas C, Kalli M, Kovalchuk NM, Simmons MJH, Matar OK, Arcucci R, Angeli Pet al., 2022,

    Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach

    , LAB ON A CHIP, Vol: 22, Pages: 3848-3859, ISSN: 1473-0197
  • Journal article
    Zhuang Y, Cheng S, Kovalchuk N, Simmons M, Matar OK, Guo Y-K, Arcucci Ret al., 2022,

    Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device

    , Lab on a Chip: miniaturisation for chemistry, physics, biology, materials science and bioengineering, Vol: 22, Pages: 3187-3202, ISSN: 1473-0189

    A major challenge in the field of microfluidics is to predict and control drop interactions. This work develops an image-based data-driven model to forecast drop dynamics based on experiments performed on a microfluidics device. Reduced-order modelling techniques are applied to compress the recorded images into low-dimensional spaces and alleviate the computational cost. Recurrent neural networks are then employed to build a surrogate model of drop interactions by learning the dynamics of compressed variables in the reduced-order space. The surrogate model is integrated with real-time observations using data assimilation. In this paper we developed an ensemble-based latent assimilation algorithm scheme which shows an improvement in terms of accuracy with respect to the previous approaches. This work demonstrates the possibility to create a reliable data-driven model enabling a high fidelity prediction of drop interactions in microfluidics device. The performance of the developed system is evaluated against experimental data (i.e., recorded videos), which are excluded from the training of the surrogate model. The developed scheme is general and can be applied to other dynamical systems.
  • Journal article
    Cheng S, Prentice IC, Huang Y, Jin Y, Guo Y-K, Arcucci Ret al., 2022,

    Data-driven surrogate model with latent data-assimilation: application to wildfire forecasting

    , Journal of Computational Physics, Vol: 464, ISSN: 0021-9991

    The large and catastrophic wildfires have been increasing across the globe in the recent decade, highlighting the importance of simulating and forecasting fire dynamics in near real-time. This is extremely challenging due to the complexities of physical models and geographical features. Running physics-based simulations for large wildfire events in near real-time are computationally expensive, if not infeasible. In this work, we develop and test a novel data-model integration scheme for fire progression forecasting, that combines Reduced-order modelling, recurrent neural networks (Long-Short-Term Memory), data assimilation, and error covariance tuning. The Reduced-order modelling and the machine learning surrogate model ensure the efficiency of the proposed approach while the data assimilation enables the system to adjust the simulation with observations. We applied this algorithm to simulate and forecast three recent large wildfire events in California from 2017 to 2020. The deep-learning-based surrogate model runs around 1000 times faster than the Cellular Automata simulation which is used to generate training data-sets. The daily fire perimeters derived from satellite observation are used as observation data in Latent Assimilation to adjust the fire forecasting in near real-time. An error covariance tuning algorithm is also performed in the reduced space to estimate prior simulation and observation errors. The evolution of the averaged relative root mean square error (R-RMSE) shows that data assimilation and covariance tuning reduce the RMSE by about 50% and considerably improves the forecasting accuracy. As a first attempt at a reduced order wildfire spread forecasting, our exploratory work showed the potential of data-driven machine learning models to speed up fire forecasting for various applications.
  • Journal article
    Lever J, Arcucci R, 2022,

    Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

    , Journal of Computational Social Science, Vol: 5, Pages: 1427-1465, ISSN: 2432-2717

    The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. We perform data collection and subsequent analysis & visualisation, and compare regional differences in online social sentiment expression. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work develops the concept of the human sensor in the context of wildfires, using users’ Tweets as noisy subjective sentimental accounts of current localised conditions. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling.
  • Journal article
    Cheng S, Jin Y, Harrison S, Quilodrán Casas C, Prentice C, Guo Y-K, Arcucci Ret al., 2022,

    Parameter flexible wildfire prediction using machine learning techniques: forward and inverse modelling

    , Remote Sensing, Vol: 14, ISSN: 2072-4292

    Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.
  • Book chapter
    Lever J, Arcucci R, 2022,

    Towards Social Machine Learning for Natural Disasters

    , Computational Science – ICCS 2022 22nd International Conference, London, UK, June 21–23, 2022, Proceedings, Part III, Publisher: Springer, Pages: 756-769, ISBN: 9783031087561

    The four-volume set LNCS 13350, 13351, 13352, and 13353 constitutes the proceedings of the 22ndt International Conference on Computational Science, ICCS 2022, held in London, UK, in June 2022.* The total of 175 full papers and 78 short ...
  • Journal article
    Schneider R, Bonavita M, Geer A, Arcucci R, Dueben P, Vitolo C, Le Saux B, Demir B, Mathieu P-Pet al., 2022,

    ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction

    , NPJ CLIMATE AND ATMOSPHERIC SCIENCE, Vol: 5, ISSN: 2397-3722
  • Journal article
    Dmitrewski A, Molina-Solana M, Arcucci R, 2022,

    CNTRLDA: A building energy management control system with real-time adjustments. Application to indoor temperature

    , BUILDING AND ENVIRONMENT, Vol: 215, ISSN: 0360-1323
  • Journal article
    Buizza C, Casas CQ, Nadler P, Mack J, Marrone S, Titus Z, Le Cornec C, Heylen E, Dur T, Ruiz LB, Heaney C, Lopez JAD, Kumar KSS, Arcucci Ret al., 2022,

    Data Learning: Integrating Data Assimilation and Machine Learning

    , JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 58, ISSN: 1877-7503
  • Conference paper
    Lever J, Arcucci R, Cai J, 2022,

    Social Data Assimilation of Human Sensor Networks for Wildfires

    , 15th ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), Publisher: ASSOC COMPUTING MACHINERY, Pages: 455-462
  • Conference paper
    Lever J, Arcucci R, 2022,

    Towards Social Machine Learning for Natural Disasters

    , 22nd Annual International Conference on Computational Science (ICCS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 756-769, ISSN: 0302-9743
  • Conference paper
    Cheng S, Quilodran-Casas C, Arcucci R, 2022,

    Reduced Order Surrogate Modelling and Latent Assimilation for Dynamical Systems

    , 22nd Annual International Conference on Computational Science (ICCS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 31-44, ISSN: 0302-9743
  • Conference paper
    Arcucci R, Casas CQ, Joshi A, Obeysekara A, Mottet L, Guo Y-K, Pain Cet al., 2022,

    Merging Real Images with Physics Simulations via Data Assimilation

    , 27th International European Conference on Parallel and Distributed Computing (Euro-Par), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 255-266, ISSN: 0302-9743
  • Journal article
    Tajnafoi G, Arcucci R, Mottet L, Vouriot C, Molina-Solana M, Pain C, Guo Y-Ket al., 2021,

    Variational Gaussian process for optimal sensor placement

    , Applications of Mathematics, Vol: 66, Pages: 287-317, ISSN: 0373-6725

    Sensor placement is an optimisation problem that has recently gained great relevance. In order to achieve accurate online updates of a predictive model, sensors are used to provide observations. When sensor location is optimally selected, the predictive model can greatly reduce its internal errors. A greedy-selection algorithm is used for locating these optimal spatial locations from a numerical embedded space. A novel architecture for solving this big data problem is proposed, relying on a variational Gaussian process. The generalisation of the model is further improved via the preconditioning of its inputs: Masked Autoregressive Flows are implemented to learn nonlinear, invertible transformations of the conditionally modelled spatial features. Finally, a global optimisation strategy extending the Mutual Information-based optimisation and fine-tuning of the selected optimal location is proposed. The methodology is parallelised to speed up the computational time, making these tools very fast despite the high complexity associated with both spatial modelling and placement tasks. The model is applied to a real three-dimensional test case considering a room within the Clarence Centre building located in Elephant and Castle, London, UK.
  • Journal article
    Wu P, Chang X, Yuan W, Sun J, Zhang W, Arcucci R, Guo Yet al., 2021,

    Fast data assimilation (FDA): Data assimilation by machine learning for faster optimize model state

    , JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 51, ISSN: 1877-7503
  • Conference paper
    Bonavita M, Arcucci R, Carrassi A, Dueben P, Geer AJ, Le Saux B, Longepe N, Mathieu P-P, Raynaud Let al., 2021,

    Machine Learning for Earth System Observation and Prediction

    , Publisher: AMER METEOROLOGICAL SOC, Pages: E710-E716, ISSN: 0003-0007
  • Journal article
    Cheng S, Pain CC, Guo Y-K, Arcucci Ret al., 2021,

    Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies

    <jats:title>Abstract</jats:title><jats:p>Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.</jats:p>
  • Journal article
    D'Amore L, Murano A, Sorrentino L, Arcucci R, Laccetti Get al., 2021,

    Toward a multilevel scalable parallel Zielonka's algorithm for solving parity games

    , CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, Vol: 33, ISSN: 1532-0626

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://www.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=1263&limit=20&respub-action=search.html Current Millis: 1732180527173 Current Time: Thu Nov 21 09:15:27 GMT 2024