66 results found
Gong H, Cheng S, Chen Z, et 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
Zhuang Y, Cheng S, Kovalchuk N, et 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.
Chagot L, Quilodran-Casas C, Kalli M, et 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
Cheng S, Prentice IC, Huang Y, et 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.
Lever J, Arcucci R, 2022, Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting, Journal of Computational Social Science, Pages: 1-39, 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.
Cheng S, Jin Y, Harrison S, et 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.
Lever J, Arcucci R, Cai J, 2022, Social Data Assimilation of Human Sensor Networks for Wildfires, Pages: 455-462
We present an implementation of a human sensor network in the context of wildfires. A human sensor network can be thought of as a socially nuanced abstraction of a physical sensing model, where social media users are considered noisy remote sensors with variable reliability and location. This allows real-time social modelling of physical events. We apply this concept to data collected from Twitter & Reddit in the context of California wildfires, performing sentimental & topical analysis over the period of a wildfire season to extract themes, sentiments and discussions. We assimilate this social media data in a predictive model trained by machine learning approaches for time series. Both Long Short Term Memory (LSTM) & AutoRegressive Integrated Moving Average (ARIMA) models are employed. We assimilate the human sensor networks, to overcome the limitations & biases exhibited by individual social media platform demographics. We implement Optimal Interpolation and Ensemble Kalman Filter architectures on our models & data. Finally we compare and evaluate performance, and discuss how these implementations could benefit current wildfire models.
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 ...
Schneider R, Bonavita M, Geer A, et 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
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
Buizza C, Casas CQ, Nadler P, et al., 2022, Data Learning: Integrating Data Assimilation and Machine Learning, JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 58, ISSN: 1877-7503
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
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
Arcucci R, Casas CQ, Joshi A, et 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
Bonavita M, Arcucci R, Carrassi A, et al., 2021, Machine Learning for Earth System Observation and Prediction, Publisher: AMER METEOROLOGICAL SOC, Pages: E710-E716, ISSN: 0003-0007
Tajnafoi G, Arcucci R, Mottet L, et 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.
Wu P, Chang X, Yuan W, et 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
Kumar P, Kalaiarasan G, Porter AE, et al., 2021, An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments., Science of the Total Environment, Vol: 756, Pages: 1-22, ISSN: 0048-9697
Particulate matter (PM) is a crucial health risk factor for respiratory and cardiovascular diseases. The smaller size fractions, ≤2.5 μm (PM2.5; fine particles) and ≤0.1 μm (PM0.1; ultrafine particles), show the highest bioactivity but acquiring sufficient mass for in vitro and in vivo toxicological studies is challenging. We review the suitability of available instrumentation to collect the PM mass required for these assessments. Five different microenvironments representing the diverse exposure conditions in urban environments are considered in order to establish the typical PM concentrations present. The highest concentrations of PM2.5 and PM0.1 were found near traffic (i.e. roadsides and traffic intersections), followed by indoor environments, parks and behind roadside vegetation. We identify key factors to consider when selecting sampling instrumentation. These include PM concentration on-site (low concentrations increase sampling time), nature of sampling sites (e.g. indoors; noise and space will be an issue), equipment handling and power supply. Physicochemical characterisation requires micro- to milli-gram quantities of PM and it may increase according to the processing methods (e.g. digestion or sonication). Toxicological assessments of PM involve numerous mechanisms (e.g. inflammatory processes and oxidative stress) requiring significant amounts of PM to obtain accurate results. Optimising air sampling techniques are therefore important for the appropriate collection medium/filter which have innate physical properties and the potential to interact with samples. An evaluation of methods and instrumentation used for airborne virus collection concludes that samplers operating cyclone sampling techniques (using centrifugal forces) are effective in collecting airborne viruses. We highlight that predictive modelling can help to identify pollution hotspots in an urban environment for the efficient collection of PM mass. This review provides
Quilodrán-Casas C, Silva VS, Arcucci R, et al., 2021, Digital twins based on bidirectional LSTM and GAN for modelling COVID-19
The outbreak of the coronavirus disease 2019 (COVID-19) has now spreadthroughout the globe infecting over 100 million people and causing the death ofover 2.2 million people. Thus, there is an urgent need to study the dynamics ofepidemiological models to gain a better understanding of how such diseasesspread. While epidemiological models can be computationally expensive, recentadvances in machine learning techniques have given rise to neural networks withthe ability to learn and predict complex dynamics at reduced computationalcosts. Here we introduce two digital twins of a SEIRS model applied to anidealised town. The SEIRS model has been modified to take account of spatialvariation and, where possible, the model parameters are based on official virusspreading data from the UK. We compare predictions from a data-correctedBidirectional Long Short-Term Memory network and a predictive GenerativeAdversarial Network. The predictions given by these two frameworks are accuratewhen compared to the original SEIRS model data. Additionally, these frameworksare data-agnostic and could be applied to towns, idealised or real, in the UKor in other countries. Also, more compartments could be included in the SEIRSmodel, in order to study more realistic epidemiological behaviour.
Afzali J, Casas CQ, Arcucci R, 2021, Latent GAN: Using a Latent Space-Based GAN for Rapid Forecasting of CFD Models, Pages: 360-372, ISSN: 0302-9743
The focus of this study is to simulate realistic fluid flow, through Machine Learning techniques that could be utilised in real-time forecasting of urban air pollution. We propose a novel Latent GAN architecture which looks at combining an AutoEncoder with a Generative Adversarial Network to predict fluid flow at the proceeding timestep of a given input, whilst keeping computational costs low. This architecture is applied to tracer flows and velocity fields around an urban city. We present a pair of AutoEncoders capable of dimensionality reduction of 3 orders of magnitude. Further, we present a pair of Generator models capable of performing real-time forecasting of tracer flows and velocity fields. We demonstrate that the models, as well as the latent spaces generated, learn and retain meaningful physical features of the domain. Despite the domain of this project being that of computational fluid dynamics, the Latent GAN architecture is designed to be generalisable such that it can be applied to other dynamical systems.
Amendola M, Arcucci R, Mottet L, et al., 2021, Data Assimilation in the Latent Space of a Convolutional Autoencoder, Pages: 373-386, ISSN: 0302-9743
Data Assimilation (DA) is a Bayesian inference that combines the state of a dynamical system with real data collected by instruments at a given time. The goal of DA is to improve the accuracy of the dynamic system making its result as real as possible. One of the most popular technique for DA is the Kalman Filter (KF). When the dynamic system refers to a real world application, the representation of the state of a physical system usually leads to a big data problem. For these problems, KF results computationally too expensive and mandates to use of reduced order modeling techniques. In this paper we proposed a new methodology we called Latent Assimilation (LA). It consists in performing the KF in the latent space obtained by an Autoencoder with non-linear encoder functions and non-linear decoder functions. In the latent space, the dynamic system is represented by a surrogate model built by a Recurrent Neural Network. In particular, an Long Short Term Memory (LSTM) network is used to train a function which emulates the dynamic system in the latent space. The data from the dynamic model and the real data coming from the instruments are both processed through the Autoencoder. We apply the methodology to a real test case and we show that the LA has a good performance both in accuracy and in efficiency.
Arcucci R, Zhu J, Hu S, et al., 2021, Deep Data Assimilation: Integrating Deep Learning with Data Assimilation, APPLIED SCIENCES-BASEL, Vol: 11
Ruiz LGB, Pegalajar MC, Arcucci R, et al., 2020, A time-series clustering methodology for knowledge extraction in energy consumption data, Expert Systems with Applications, Vol: 160, ISSN: 0957-4174
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.
Mack J, Arcucci R, Molina-Solana M, et al., 2020, Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 372, ISSN: 0045-7825
Wang S, Nadler P, Arcucci R, et al., 2020, A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19, IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, Vol: 15, Pages: 23-33, ISSN: 1556-603X
D'Amore L, Murano A, Sorrentino L, et al., 2020, Toward a multilevel scalable parallel Zielonka's algorithm for solving parity games, CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, Vol: 33, ISSN: 1532-0626
Nadler P, Wang S, Arcucci R, et 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.
Dur TH, Arcucci R, Mottet L, et al., 2020, Weak Constraint Gaussian Processes for optimal sensor placement, JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 42, ISSN: 1877-7503
Wu P, Sun J, Chang X, et al., 2020, Data-driven reduced order model with temporal convolutional neural network, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 360, ISSN: 0045-7825
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