Philippe Very

Gabriel Jarry, Ramon Dalmau, Xavier Olive et al.

Accurate aircraft trajectory prediction is critical for air traffic management, airline operations, and environmental assessment. This paper introduces NODE-FDM, a Neural Ordinary Differential Equations-based Flight Dynamics Model trained on Quick Access Recorder (QAR) data. By combining analytical kinematic relations with data-driven components, NODE-FDM achieves a more accurate reproduction of recorded trajectories than state-of-the-art models such as a BADA-based trajectory generation methodology (BADA4 performance model combined with trajectory control routines), particularly in the descent phase of the flight. The analysis demonstrates marked improvements across altitude, speed, and mass dynamics. Despite current limitations, including limited physical constraints and the limited availability of QAR data, the results demonstrate the potential of physics-informed neural ordinary differential equations as a high-fidelity, data-driven approach to aircraft performance modelling. Future work will extend the framework to incorporate a full modelling of the lateral dynamics of the aircraft.

Gabriel Jarry, Ramon Dalmau, Philippe Very et al.

Accurate modelling of aircraft fuel-flow is crucial for both operational planning and environmental impact assessment, yet standard parametric models often neglect performance deterioration that occurs as aircraft age. This paper investigates multiple approaches to integrate engine ageing effects into fuel-flow prediction for the Airbus A320-214, using a comprehensive dataset of approximately nineteen thousand Quick Access Recorder flights from nine distinct airframes with varying years in service. We systematically evaluate classical physics-based models, empirical correction coefficients, and data-driven neural network architectures that incorporate age either as an input feature or as an explicit multiplicative bias. Results demonstrate that while baseline models consistently underestimate fuel consumption for older aircraft, the use of age-dependent correction factors and neural models substantially reduces bias and improves prediction accuracy. Nevertheless, limitations arise from the small number of airframes and the lack of detailed maintenance event records, which constrain the representativeness and generalization of age-based corrections. This study emphasizes the importance of accounting for the effects of ageing in parametric and machine learning frameworks to improve the reliability of operational and environmental assessments. The study also highlights the need for more diverse datasets that can capture the complexity of real-world engine deterioration.

Gabriel Jarry, Ramon Dalmau, Philippe Very et al.

Aviation's climate impact includes not only CO2 emissions but also significant non-CO2 effects, especially from contrails. These ice clouds can alter Earth's radiative balance, potentially rivaling the warming effect of aviation CO2. Physics-based models provide useful estimates of contrail formation and climate impact, but their accuracy depends heavily on the quality of atmospheric input data and on assumptions used to represent complex processes like ice particle formation and humidity-driven persistence. Observational data from remote sensors, such as satellites and ground cameras, could be used to validate and calibrate these models. However, existing datasets don't explore all aspect of contrail dynamics and formation: they typically lack temporal tracking, and do not attribute contrails to their source flights. To address these limitations, we present the Ground Visible Camera Contrail Sequences (GVCCS), a new open data set of contrails recorded with a ground-based all-sky camera in the visible range. Each contrail is individually labeled and tracked over time, allowing a detailed analysis of its lifecycle. The dataset contains 122 video sequences (24,228 frames) and includes flight identifiers for contrails that form above the camera. As reference, we also propose a unified deep learning framework for contrail analysis using a panoptic segmentation model that performs semantic segmentation (contrail pixel identification), instance segmentation (individual contrail separation), and temporal tracking in a single architecture. By providing high-quality, temporally resolved annotations and a benchmark for model evaluation, our work supports improved contrail monitoring and will facilitate better calibration of physical models. This sets the groundwork for more accurate climate impact understanding and assessments.

Ramon Dalmau, Gabriel Jarry, Philippe Very

Aviation's non-CO2 effects, particularly contrails, are a significant contributor to its climate impact. Persistent contrails can evolve into cirrus-like clouds that trap outgoing infrared radiation, with radiative forcing potentially comparable to or exceeding that of aviation's CO2 emissions. While physical models simulate contrail formation, evolution and dissipation, validating and calibrating these models requires linking observed contrails to the flights that generated them, a process known as contrail-to-flight attribution. Satellite-based attribution is challenging due to limited spatial and temporal resolution, as contrails often drift and deform before detection. In this paper, we evaluate an alternative approach using ground-based cameras, which capture contrails shortly after formation at high spatial and temporal resolution, when they remain thin, linear, and visually distinct. Leveraging the ground visible camera contrail sequences (GVCCS) dataset, we introduce a modular framework for attributing contrails observed using ground-based cameras to theoretical contrails derived from aircraft surveillance and meteorological data. The framework accommodates multiple geometric representations and distance metrics, incorporates temporal smoothing, and enables flexible probability-based assignment strategies. This work establishes a strong baseline and provides a modular framework for future research in linking contrails to their source flight.

Victor Bouvier, Philippe Very, Clément Chastagnol et al.

Unsupervised Domain Adaptation (UDA) aims to bridge the gap between a source domain, where labelled data are available, and a target domain only represented with unlabelled data. If domain invariant representations have dramatically improved the adaptability of models, to guarantee their good transferability remains a challenging problem. This paper addresses this problem by using active learning to annotate a small budget of target data. Although this setup, called Active Domain Adaptation (ADA), deviates from UDA's standard setup, a wide range of practical applications are faced with this situation. To this purpose, we introduce \textit{Stochastic Adversarial Gradient Embedding} (SAGE), a framework that makes a triple contribution to ADA. First, we select for annotation target samples that are likely to improve the representations' transferability by measuring the variation, before and after annotation, of the transferability loss gradient. Second, we increase sampling diversity by promoting different gradient directions. Third, we introduce a novel training procedure for actively incorporating target samples when learning invariant representations. SAGE is based on solid theoretical ground and validated on various UDA benchmarks against several baselines. Our empirical investigation demonstrates that SAGE takes the best of uncertainty \textit{vs} diversity samplings and improves representations transferability substantially.

Victor Bouvier, Philippe Very, Clément Chastagnol et al.

Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in the last ten years. The emergence of Domain Invariant Representations (IR) has improved drastically the transferability of representations from a labelled source domain to a new and unlabelled target domain. However, a potential pitfall of this approach, namely the presence of \textit{label shift}, has been brought to light. Some works address this issue with a relaxed version of domain invariance obtained by weighting samples, a strategy often referred to as Importance Sampling. From our point of view, the theoretical aspects of how Importance Sampling and Invariant Representations interact in UDA have not been studied in depth. In the present work, we present a bound of the target risk which incorporates both weights and invariant representations. Our theoretical analysis highlights the role of inductive bias in aligning distributions across domains. We illustrate it on standard benchmarks by proposing a new learning procedure for UDA. We observed empirically that weak inductive bias makes adaptation more robust. The elaboration of stronger inductive bias is a promising direction for new UDA algorithms.

Victor Bouvier, Philippe Very, Céline Hudelot et al.

Learning representations which remain invariant to a nuisance factor has a great interest in Domain Adaptation, Transfer Learning, and Fair Machine Learning. Finding such representations becomes highly challenging in NLP tasks since the nuisance factor is entangled in a raw text. To our knowledge, a major issue is also that only few NLP datasets allow assessing the impact of such factor. In this paper, we introduce two generalization metrics to assess model robustness to a nuisance factor: \textit{generalization under target bias} and \textit{generalization onto unknown}. We combine those metrics with a simple data filtering approach to control the impact of the nuisance factor on the data and thus to build experimental biased datasets. We apply our method to standard datasets of the literature (\textit{Amazon} and \textit{Yelp}). Our work shows that a simple text classification baseline (i.e., sentiment analysis on reviews) may be badly affected by the \textit{product ID} (considered as a nuisance factor) when learning the polarity of a review. The method proposed is generic and applicable as soon as the nuisance variable is annotated in the dataset.

Victor Bouvier, Philippe Very, Céline Hudelot et al.

Unsupervised Domain Adaptation aims to learn a model on a source domain with labeled data in order to perform well on unlabeled data of a target domain. Current approaches focus on learning \textit{Domain Invariant Representations}. It relies on the assumption that such representations are well-suited for learning the supervised task in the target domain. We rather believe that a better and minimal assumption for performing Domain Adaptation is the \textit{Hidden Covariate Shift} hypothesis. Such approach consists in learning a representation of the data such that the label distribution conditioned on this representation is domain invariant. From the Hidden Covariate Shift assumption, we derive an optimization procedure which learns to match an estimated joint distribution on the target domain and a re-weighted joint distribution on the source domain. The re-weighting is done in the representation space and is learned during the optimization procedure. We show on synthetic data and real world data that our approach deals with both \textit{Target Shift} and \textit{Concept Drift}. We report state-of-the-art performances on Amazon Reviews dataset \cite{blitzer2007biographies} demonstrating the viability of this approach.

Gautier Marti, Philippe Donnat, Frank Nielsen et al.

We describe a new visualization tool, dubbed HCMapper, that visually helps to compare a pair of dendrograms computed on the same dataset by displaying multiscale partition-based layered structures. The dendrograms are obtained by hierarchical clustering techniques whose output reflects some hypothesis on the data and HCMapper is specifically designed to grasp at first glance both whether the two compared hypotheses broadly agree and the data points on which they do not concur. Leveraging juxtaposition and explicit encodings, HCMapper focus on two selected partitions while displaying coarser ones in context areas for understanding multiscale structure and eventually switching the selected partitions. HCMapper utility is shown through the example of testing whether the prices of credit default swap financial time series only undergo correlation. This use case is detailed in the supplementary material as well as experiments with code on toy-datasets for reproducible research. HCMapper is currently released as a visualization tool on the DataGrapple time series and clustering analysis platorm at www.datagrapple.com.

Gautier Marti, Philippe Very, Philippe Donnat

This paper presents a pre-processing and a distance which improve the performance of machine learning algorithms working on independent and identically distributed stochastic processes. We introduce a novel non-parametric approach to represent random variables which splits apart dependency and distribution without losing any information. We also propound an associated metric leveraging this representation and its statistical estimate. Besides experiments on synthetic datasets, the benefits of our contribution is illustrated through the example of clustering financial time series, for instance prices from the credit default swaps market. Results are available on the website www.datagrapple.com and an IPython Notebook tutorial is available at www.datagrapple.com/Tech for reproducible research.