1,720,999 research outputs found
Multi-fidelity surrogate modeling using long short-term memory networks
Full text linkWhen evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between accuracy and efficiency. Especially for parametrized, time-dependent problems in engineering computations, it is often the case that acceptable computational budgets limit the availability of high-fidelity, accurate simulation data. Multi-fidelity surrogate modeling has emerged as an effective strategy to overcome this difficulty. Its key idea is to leverage many low-fidelity simulation data, less accurate but much faster to compute, to improve the approximations with limited high-fidelity data. In this work, we introduce a novel data-driven framework of multi-fidelity surrogate modeling for parametrized, time-dependent problems using long short-term memory (LSTM) networks, to enhance output predictions both for unseen parameter values and forward in time simultaneously - a task known to be particularly challenging for data-driven models. We demonstrate the wide applicability of the proposed approaches in a variety of engineering problems with high-and low-fidelity data generated through fine versus coarse meshes, small versus large time steps, or finite element full order versus deep learning reduced-order models. Numerical results show that the proposed multi-fidelity LSTM networks not only improve single-fidelity regression significantly, but also outperform the multi-fidelity models based on feed-forward neural networks
Multi-fidelity reduced-order surrogate modelling
No full textHigh-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations considered and/or time window evaluated. Multi-fidelity surrogate modelling aims to leverage less accurate, lower-fidelity models that are computationally inexpensive in order to enhance predictive accuracy when high-fidelity data are scarce. However, low-fidelity models, while often displaying the qualitative solution behaviour, fail to accurately capture fine spatio-temporal and dynamic features of high-fidelity models. To address this shortcoming, we present a data-driven strategy that combines dimensionality reduction with multifidelity neural network surrogates. The key idea is to generate a spatial basis by applying proper orthogonal decomposition (POD) to high-fidelity solution snapshots, and approximate the dynamics of the reduced states—time-parameter-dependent expansion coefficients of the POD basis—using a multi-fidelity long short-term memory network. By mapping low-fidelity reduced states to their high-fidelity counterpart, the proposed reduced-order surrogate model enables the efficient recovery of full solution fields over time and parameter variations in a non-intrusive manner. The generality of this method is demonstrated by a collection of PDE problems where the low-fidelity model can be defined by coarser meshes and/or time stepping, as well as by misspecified physical features
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Latent model discovery for efficient simulation and uncertainty quantification of wildfire dynamics
No full textLAUREA MAGISTRALEGli incendi boschivi sono tra gli eventi naturali più catastrofici, causando gravi danni economici e, soprattutto, perdite di vite umane. Diversi modelli computazionali sono in grado di simulare accuratamente l'evoluzione dei fronti di fiamma; tuttavia, il loro elevato costo computazionale li rende inadatti a previsioni in real-time in molteplici scenari. Le previsioni in tempo reale renderebbero questi modelli strumenti preziosi per supportare compiti chiave come la soppressione attiva degli incendi, le operazioni di contenimento e la pianificazione di protocolli di evacuazione.
Questa tesi esplora l'uso di modelli di ordine ridotto (ROM) per migliorare l'efficienza computazionale riducendo la dimensionalità del problema. Vengono esaminati diversi approcci, a partire dagli autoencoder convoluzionali per la riduzione della dimensionalità spaziale, seguiti dalle equazioni differenziali ordinarie neurali (ODE neurali) per la scoperta delle dinamiche latenti. L'accuratezza predittiva viene ulteriormente migliorata utilizzando un approccio multi-fidelity, sfruttando i dati a bassa fedeltà come caratteristiche aggiuntive in un modello basato su transformers.
Infine, applichiamo questi modelli in un contesto di quantificazione dell'incertezza (UQ), assegnando distribuzioni di probabilità ai parametri di input chiave e utilizzando un approccio Monte Carlo per stimare i momenti delle quantità di interesse, per tenere conto dell'impatto delle condizioni di incertezza sui risultati della simulazione. Sebbene il Multi-Fidelity Transformer raggiunga la massima accuratezza predittiva, i nostri risultati suggeriscono che nessun modello è universalmente ottimale: il modello più performante dipende dal compromesso tra accuratezza e tempo di calcolo, che varia a seconda dei casi d'uso e delle quantità target.Wildfires are among the most catastrophic natural events, causing severe economic damage and, more importantly, loss of life. Several computational models can accurately simulate the evolution of wildfire fronts; however, their high computational cost makes them unsuitable for real-time predictions in multiple virtual scenarios. Real-time predictions would make these models valuable tools to support key tasks like active fire suppression, containment operations, and evacuation planning.
This thesis explores the use of Reduced Order Models (ROMs) to enhance computational efficiency by reducing the dimensionality of the problem. Different approaches are investigated, starting with convolutional autoencoders for spatial dimensionality reduction, followed by Neural Ordinary Differential Equations (Neural ODEs) for latent dynamics discovery. The predictive accuracy is further enhanced by performing data fusion in the spirit of multi-fidelity neural network regression, leveraging low-fidelity data as additional features in a transformer-based model.
Finally, we apply these models in an Uncertainty Quantification (UQ) framework, by assigning probability distributions to key input parameters and using a Monte Carlo approach to estimate moments of quantities of interest to account for the impact of uncertain conditions on the simulation outcomes. Although the Multi-Fidelity Transformer achieves the highest predictive accuracy, our results suggest that no single model is universally optimal, with the best-performing model depending on the trade-off between accuracy and computational time, which varies across different use cases and target quantities
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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