Imperial College London

DrRossellaArcucci

Faculty of EngineeringDepartment of Earth Science & Engineering

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Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

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69 results found

Arcucci R, Xiao D, Fang F, Navon IM, Wu P, Pain CC, Guo YKet al., 2023, A reduced order with data assimilation model: Theory and practice, Computers and Fluids, Vol: 257, ISSN: 0045-7930

Numerical simulations are extensively 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. Fast-running Non-Intrusive Reduced Order Model (NIROM) for predicting the turbulent air flows has been proved to be an efficient method to provide numerical forecasting results. However, due to the reduced space on which the model operates, the solution includes uncertainties. Additionally, any computational methodology contributes to uncertainty due to finite precision and the consequent accumulation and amplification of round-off errors. Taking into account these uncertainties is essential for the acceptance of any numerical simulation. In this paper we combine the NIROM method with Data Assimilation (DA), the main question is how to incorporate data (e.g. from physical measurements) in models in a suitable way, in order to improve model predictions and quantify prediction uncertainty. Here, the focus is on the prediction of nonlinear dynamical systems (the classical application example being weather forecasting). DA is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. The Reduced Order Data Assimilation (RODA) model we propose in this paper achieves both efficiency and accuracy including Variational DA into NIROM. The model we present is applied to the pollutant dispersion within an urban environment.

Journal article

Quilodrán-Casas C, Arcucci R, 2023, A data-driven adversarial machine learning for 3D surrogates of unstructured computational fluid dynamic simulations, Physica A: Statistical Mechanics and its Applications, Vol: 615, ISSN: 0378-4371

This paper presents a general workflow to generate and improve the forecast of model surrogates of computational fluid dynamics simulations using deep learning, and most specifically adversarial training. This adversarial approach aims to reduce the divergence of the forecasts from the underlying physical model. Our two-step method integrates a Principal Components Analysis (PCA) based adversarial autoencoder (PC-AAE) with adversarial Long short-term memory (LSTM) networks. Once the reduced-order model (ROM) of the CFD solution is obtained via PCA, an adversarial autoencoder is used on the principal components time series. Subsequentially, a LSTM is adversarially trained on the latent space produced by the PC-AAE to make forecasts. Here we show, that the application of adversarial training improves the rollout of the latent space predictions. Our workflow is applied to three different case studies including two models of urban air pollution in London.

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, ISSN: 0885-7474

Journal article

Cheng S, Pain CC, Guo YK, Arcucci Ret al., 2023, Real-time updating of dynamic social networks for COVID-19 vaccination strategies, Journal of Ambient Intelligence and Humanized Computing, ISSN: 1868-5137

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.

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

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

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

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.

Journal article

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.

Conference paper

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 ...

Book chapter

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

Browne P, Lima A, Arcucci R, Quilodrán-Casas Cet al., 2022, Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations

Starting from the Kaya identity, we used a Neural ODE model to predict theevolution of several indicators related to carbon emissions, on acountry-level: population, GDP per capita, energy intensity of GDP, carbonintensity of energy. We compared the model with a baseline statistical model -VAR - and obtained good performances. We conclude that this machine-learningapproach can be used to produce a wide range of results and give relevantinsight to policymakers

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

Journal article

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

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

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

Conference paper

Khandelwal P, Nadler P, Arcucci R, Knottenbelt W, Guo Y-Ket al., 2021, A Scalable Inference Method For Large Dynamic Economic Systems

The nature of available economic data has changed fundamentally in the lastdecade due to the economy's digitisation. With the prevalence of often blackbox data-driven machine learning methods, there is a necessity to developinterpretable machine learning methods that can conduct econometric inference,helping policymakers leverage the new nature of economic data. We thereforepresent a novel Variational Bayesian Inference approach to incorporate atime-varying parameter auto-regressive model which is scalable for big data.Our model is applied to a large blockchain dataset containing prices,transactions of individual actors, analyzing transactional flows and pricemovements on a very granular level. The model is extendable to any datasetwhich can be modelled as a dynamical system. We further improve the simplestate-space modelling by introducing non-linearities in the forward model withthe help of machine learning architectures.

Journal article

Hendrickx R, Arcucci R, Lopez JAD, Guo Y-K, Kennedy Met al., 2021, Correcting public opinion trends through Bayesian data assimilation

Measuring public opinion is a key focus during democratic elections, enablingcandidates to gauge their popularity and alter their campaign strategiesaccordingly. Traditional survey polling remains the most popular estimationtechnique, despite its cost and time intensity, measurement errors, lack ofreal-time capabilities and lagged representation of public opinion. In recentyears, Twitter opinion mining has attempted to combat these issues. Despiteachieving promising results, it experiences its own set of shortcomings such asan unrepresentative sample population and a lack of long term stability. Thispaper aims to merge data from both these techniques using Bayesian dataassimilation to arrive at a more accurate estimate of true public opinion forthe Brexit referendum. This paper demonstrates the effectiveness of theproposed approach using Twitter opinion data and survey data from trustedpollsters. Firstly, the possible existence of a time gap of 16 days between thetwo data sets is identified. This gap is subsequently incorporated into aproposed assimilation architecture. This method was found to adequatelyincorporate information from both sources and measure a strong upward trend inLeave support leading up to the Brexit referendum. The proposed techniqueprovides useful estimates of true opinion, which is essential to future opinionmeasurement and forecasting research.

Journal article

Quilodrán-Casas C, Arcucci R, Mottet L, Guo Y, Pain Cet al., 2021, Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations

This paper presents an approach to improve the forecast of computationalfluid dynamics (CFD) simulations of urban air pollution using deep learning,and most specifically adversarial training. This adversarial approach aims toreduce the divergence of the forecasts from the underlying physical model. Ourtwo-step method integrates a Principal Components Analysis (PCA) basedadversarial autoencoder (PC-AAE) with adversarial Long short-term memory (LSTM)networks. Once the reduced-order model (ROM) of the CFD solution is obtainedvia PCA, an adversarial autoencoder is used on the principal components timeseries. Subsequentially, a Long Short-Term Memory network (LSTM) isadversarially trained on the latent space produced by the PC-AAE to makeforecasts. Once trained, the adversarially trained LSTM outperforms a LSTMtrained in a classical way. The study area is in South London, includingthree-dimensional velocity vectors in a busy traffic junction.

Journal article

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

Conference paper

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

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

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

Journal article

Kumar P, Kalaiarasan G, Porter AE, Pinna A, Kłosowski MM, Demokritou P, Chung KF, Pain C, Arvind DK, Arcucci R, Adcock IM, Dilliway Cet 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

Journal article

Quilodrán-Casas C, Silva VS, Arcucci R, Heaney CE, Guo Y, Pain CCet 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.

Journal article

Quilodrán-Casas C, Arcucci R, Pain C, Guo Yet al., 2021, Adversarially trained LSTMs on reduced order models of urban air pollution simulations

This paper presents an approach to improve computational fluid dynamicssimulations forecasts of air pollution using deep learning. Our method, whichintegrates Principal Components Analysis (PCA) and adversarial training, is away to improve the forecast skill of reduced order models obtained from theoriginal model solution. Once the reduced-order model (ROM) is obtained viaPCA, a Long Short-Term Memory network (LSTM) is adversarially trained on theROM to make forecasts. Once trained, the adversarially trained LSTM outperformsa LSTM trained in a classical way. The study area is in London, includingvelocities and a concentration tracer that replicates a busy traffic junction.This adversarially trained LSTM-based approach is used on the ROM in order toproduce faster forecasts of the air pollution tracer.

Journal article

Amendola M, Arcucci R, Mottet L, Casas CQ, Fan S, Pain C, Linden P, Guo YKet 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.

Conference paper

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