Active Learning Metamodels for ATM Simulation Modeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables and their interrelationships, unknown stochastic phenomena, and ultimately human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their obvious advantages,simulation models can still end up being quite complex themselves. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves laborious and systematic analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values

Active Learning for Air Traffic Management Simulation Metamodeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables, corresponding interrelationships, and the unpredictability of human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their clear advantages, these models can too suffer from high complexity and computational hindrances, especially when designed along with fine detail. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves exhausting and manual-driven intense analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate, via fast functions, the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values given a pre-specified input region

Explainable Metamodels for ATM Performance Assessment

Fast-time simulation constitutes a well-known and long-established technique within the Air Traffic Management (ATM) community. However, it is often the case that simulation input and output spaces are underutilized, limiting the full understandability, transparency, and interpretability of the obtained results. In this paper, we propose a methodology that combines simulation metamodeling and SHapley Additive exPlanations (SHAP) values, aimed at uncovering the intricate hidden relationships among the input and output variables of a simulated ATM system in a rather practical way. Whereas metamodeling provides explicit functional approximations mimicking the behavior of the simulators, the SHAP-based analysis delivers a systematic framework for improving their explainability. We illustrate our approach using a state-of-the-art ATM simulator across two case studies in which two delay-centered performance metrics are analyzed. The results show that the proposed methodology can effectively make simulation and its results more explainable, facilitating the interpretation of the obtained emergent behavior, and additionally opening new opportunities towards novel performance assessment processes within the ATM research field

NOSTROMO - D5.2 - ATM Performance Metamodels - Final Release

This deliverable presents the third iteration of the development of the two micromodels Flitan and Mercury and the results obtained with them with the active learning process, as described in the deliverables D3.X. In this iteration, Flitan implemented concepts from PJ08.01 and PJ02.08, and Mercury implemented a module related to PJ07.02. Mercury also developed an additional module related to PJ01.01, which description is presented in Annex only, since no results could be produced in time with it for this deliverable. The development is presented in two different chapters for each simulator, with general descriptions referred to from D5.1. The modules related to each SESAR solution are described separately. The latest version of the meta-modelling process is described briefly, followed by the results obtained with the two simulators, in distinct sections. This chapter shows the performance of the meta-model with respect to approximating micro simulators

Renormalization group and spectra of the generalized P\"oschl-Teller potential

We study the P\"oschl-Teller potential $V(x) = \alpha^2 g_s \sinh^{-2}(\alpha x) + \alpha^2 g_c \cosh^{-2}(\alpha x)$, for every value of the dimensionless parameters $g_s$ and $g_c$, including the less usual ranges for which the regular singularity at the origin prevents the Hamiltonian from being self-adjoint. We apply a renormalization procedure to obtain a family of well-defined energy eigenfunctions, and study the associated renormalization group (RG) flow. We find an anomalous length scale that appears by dimensional transmutation, and spontaneously breaks the asymptotic conformal symmetry near the singularity, which is also explicitly broken by the dimensionful parameter $\alpha$ in the potential. These two competing ways of breaking conformal symmetry give the RG flow a rich structure, with phenomena such as a possible region of walking coupling, massive phases, and non-trivial limits even when the anomalous dimension is absent. We show that supersymmetry of the potential, when present, is also spontaneously broken, along with asymptotic conformal symmetry. We use the family of eigenfunctions to compute the S-matrix in all regions of parameter space, for any value of anomalous scale, and systematically study the poles of the S-matrix to classify all bound, anti-bound and metastable states, including quasi-normal modes. The anomalous scale, as expected, changes the spectra in non-trivial ways.Comment: 42 pages, 16 figures. V2 - Improved version: new discussions added in Sect.4, introduction and conclusio

NOSTROMO - D1.2 - Final Project Results Report

The main objective of the NOSTROMO project has been to develop, demonstrate and evaluate an innovative modelling approach for the rigorous and comprehensive assessment of the performance impact of future ATM concepts and solutions at ECAC network level. This approach brings together the ability of bottom-up microscopic models to capture emergent behaviour and interdependencies between different solutions with the level of tractability and interpretability required to effectively support decision-making. This report provides a summary of NOSTROMO accomplishments and contributions to the SESAR Programme. It gathers technical lessons learned and concludes proposing further developments to facilitate the use of the NOSTROMO methodology in the future SESAR 3 Programme

Diagnóstico socioeconômico-cultural e ambiental dos municípios do Projeto Boa Esperança.

A Embrapa Meio-Norte, por iniciativa da Chesf, com a qual estabeleceu uma parceria, vem desenvolvendo o Projeto Boa Esperança, visando resultar na melhoria dos níveis de produtividade e renda da atividade agropecuária, de forma participativa, além da mitigação da degradação ambiental. A importância desse projeto baseia-se na interação entre pesquisadores, extensionistas, agricultores familiares e pescadores organizados das comunidades, objetos de intervenção por intermédio do processo de difusão e transferência de alternativas tecnológicas. O projeto é composto de vários planos de ação, dos quais um pretende avaliar os aspectos sociotécnico-culturaleconômicos dos membros das comunidades, objeto das ações propostas no projeto, bem como levantar e organizar um sistema de informação de mercado da produção agrícola familiar em apoio ao projeto na geração de renda. Para a realização dos objetivos propostos, o projeto prevê que sejam realizados três diagnósticos participativos, no início, no meio e no final da sua execução, nas diversas comunidades da área de sua abrangência, onde se pretende implementar as atividades, com aplicação de questionário sociotécnico-cultural-econômico para identificação dos tipos de sistemas praticados, evolução da adoção das tecnologias e resultados finais obtidos pelos agricultores familiares e pescadores. Este documento traz o resultado do trabalho de prospecção realizado nas comunidades do entorno da Barragem de Boa Esperança, o qual contém dados sobre o agricultor/pescador, sua família e suas atividades, considerando o momento inicial de implementação do projeto.bitstream/item/83619/1/Doc-202-Diagnostico-completo.pd

Fungos micorrízicos arbusculares em dois fragmentos florestais de restinga periodicamente inundável em Marambaia, RJ

O presente estudo objetivou avaliar a ocorrência de fungos micorrízicos arbusculares (FMA) em dois fragmentos florestais (FF 1 e FF 2) de Restinga periodicamente inundável, dispostos em um gradiente de saturação hídrica do solo em Marambaia, RJ. Em cada área foram coletadas amostras da camada superficial (0-5 cm), para avaliação da comunidade de FMA e dos atributos do solo. Entre as seis espécies de FMA encontradas, cinco foram comuns a ambas as áreas eAcaulospora scrobiculata se restringiu a FF 1.Acaulospora e Glomus foram os gêneros mais adaptados às condições ambientais dos ecossistemas. Não houve diferença significativa entre os fragmentos quanto à riqueza de espécies de FMA. Contudo, a abundância de esporos foi maior no FF 1. Parte destes resultados pode ser um reflexo dos menores valores de P disponível, teor de água e temperatura do solo no FF 1, quando comparado ao FF 2