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Fading regularization method for an inverse boundary value problem associated with the biharmonic equation
No full textInternational audienceIn this paper, we propose a numerical algorithm that combines the fading regularization method with the method of fundamental solutions (MFS) to solve a Cauchy problem associated with the biharmonic equation. We introduce a new stopping criterion for the iterative process and compare its performance with previous criteria. Numerical simulations using MFS validate the accuracy of this stopping criterion for both compatible and noisy data and demonstrate the convergence, stability, and efficiency of the proposed algorithm, as well as its ability to deblur noisy data
Modeling Solute Transport in Rivers: Analytical and Numerical Solutions
No full textInternational audienceThis study presents a novel analytical framework for modeling one-dimensio- nal solute transport in rivers, integrating advection, rate-limited adsorption on suspended sediments, and first-order degradation. Analytical solutions are used to validate the numerical scheme’s accuracy under idealized conditions, tested for instantaneous and continuous pollutant discharges. The research importantly investigates short-term solute accumulation in riverbeds, a crit- ical yet understudied process that affects sediment transport and pollutant fate. Applicable to a wide range of contaminants (e.g., nutrients, pesti- cides), the framework aids water quality assessment, pollution control, and risk mitigation. Implemented in the open-source SWASHES library, these solutions provide practical tools for decision-support systems and serve as reliable benchmarks to validate numerical models. By addressing transient and persistent pollutant scenarios, this work enhances predictive capabili- ties for environmental management. The approach bridges analytical and numerical methods, offering a robust foundation for simulating solute trans- port across industrial and ecological contexts, advancing sustainable water resource management
High-resolution building stock energy model for assessing the impact on the load curve of residential space heating electrification
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Rapid and reversible plasticity of upper thermal limit, but no effects of multigenerational warming in medaka
No full textInternational audienceThe global rise of atmospheric temperature and the escalating occurrence of extreme climatic events urge for a better understanding of the factors influencing thermal tolerance to extreme temperatures. In particular, the critical thermal maximum (CTmax) is a proxy of the upper thermal tolerance that is key in assessing species vulnerability to warming. While several mechanisms, such as short-term acclimation and intergenerational effects, can influence the CTmax of ectotherm species, their relative contributions and interactions under natural conditions remain unclear. Using the medaka fish (Oryzias latipes), this study investigates whether multigenerational warming has shaped CTmax after multiple generations of laboratory rearing and explores the interplay between potential intergenerational effects and plastic responses during an artificial heatwave in mesocosms. Our findings reveal that seven years of multigenerational exposure to different temperatures (30 • C vs. 20 • C) did not cause any detectable durable change in CTmax. This suggests that evolution of CTmax is slow or requires strong selective pressures to evolve fast enough. In contrast, medaka fish exhibited rapid acclimation with a 1.5 • C increase in their CTmax following a two-week heatwave. Interestingly, two weeks after the heatwaves, we could not detect a significant effect of heatwave exposure on CTmax showing that this plastic response can reverse rapidly. These results, along with other studies on ectotherms thermal tolerance, emphasize the potential for short-term response of species to changing climate through acclimation. Our findings highlight the importance of integrating plasticity into vulnerability assessments to improve predictions of climate change impacts
Statistical study of displacement cascades in Ni and FeNiCr alloys: Understanding the influence of potential and composition on primary damage modeling
No full textInternational audienceThis work investigates the interaction between high-energy particles and metals, focusing on the primary irradiation damage through extensive molecular dynamics simulations. The cascades are simulated using empirical interatomic potentials and cover an extensive range of energies (above and below the sub cascade threshold), ranging from 0.5 keV to 120 keV. These potentials are characterized using properties associated with point defects, surface energy, stacking fault energy, threshold displacement energy, and Quasi-Static Drag (QSD). The data obtained from the simulations are analyzed using specific descriptors, which helps improve our understanding of the primary damage.A database containing approximately 15,000 displacements cascades in both nickel (Ni) and the FeNiCr alloy has been generated by molecular dynamics. To assess these extensive datasets, a variety of statistical methodologies, including MANOVA, ANOVA, k means and correlation matrices, were employed. Utilizing these analytical tools and statistical descriptors, the study investigated the influences of potentials and compositions on defect production in both nickel (with 3 different Ni potentials) and 5 different FeNiCr compositions. A comparative analysis between the outcomes of potential and alloy analyses was conducted to determine the predominant effect.Potentials exhibit varied effects, particularly post-fragmentation energy, influencing cascade fragmentation and mono defects. Alloy compositions showcase differing defect production patterns, with Ni generating more defects, while alloys produce an elevated number of mono-interstitials and interstitial clusters. Notably, the study highlights the impact of the Ni fragmentation energy, identifying differing effects below and above this threshold, with a pronounced influence on interstitials
On a high-order shallow-water wave model with canonical non-local Hamiltonian structure
No full textInternational audienceWe derive and study a new family of non-local partial differential equations (PDEs) that model free-surface long gravity waves over a flat bottom. To derive the model equations we approximate the velocity potential as a series of vertical polynomials derived from the shallow-water expansion of the Dirichlet-to-Neumann problem in the Hamiltonian formulation of free-surface potential flow and invoke Luke's variational principle. The resulting evolution equations exhibit a non-local Hamiltonian structure being coupled with a system of linear elliptic spatial PDEs on the horizontal plane. A key advantage of this approach is that it directly yields canonical Hamiltonian equations, which are well-suited for numerical solutions using standard methods. This class of model equations offers high-order shallow-water approximations of the water-wave problem. It contains terms whose spatial derivatives are at most of order two, distinguishing it from asymptotic methods involving higher-order mixed spatio-temporal derivatives. We explore the first non-trivial member of this family, highlighting its connections to other mathematical models and emphasizing its practical utility. We then analyze and discuss its linear dispersive properties and demonstrate that it does not exhibit a specific type of instability known as wave-trough instability. Additionally, we demonstrate its effectiveness in simulating the long-distance steady propagation of strongly non-linear solitary waves and the head-on collision of two counter-propagating solitary waves. In the latter case, comparisons with experimental data confirm the model's ability to capture complex wave dynamics, including wave transformation in the presence of strong non-linearity and dispersion. The extension of this approach to accommodate variable bottom topography is briefly discussed
A SYNTHETIC DATASET OF FRENCH ELECTRIC LOAD CURVES WITH TEMPERATURE CONDITIONING
No full textWorkshop paper at "Tackling Climate Change with Machine Learning", ICLR 2025International audienceThe undergoing energy transition is causing behavioral changes in electricity use, e.g. with self-consumption of local generation, or flexibility services for demand control. To better understand these changes and the challenges they induce, accessing individual smart meter data is crucial. Yet this is personal data under the European GDPR. A widespread use of such data requires thus to create synthetic realistic and privacy-preserving samples. This paper introduces a new synthetic load curve dataset generated by conditional latent diffusion. We also provide the contracted power, time-of-use plan and local temperature used for generation. Fidelity, utility and privacy of the dataset are thoroughly evaluated, demonstrating its good quality and thereby supporting its interest for energy modeling applications
Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series
No full textInternational audienceImproving smart grid system management is crucial in the fight against climate change, and enabling consumers to play an active role in this effort is a significant challenge for electricity suppliers. In this regard, millions of smart meters have been deployed worldwide in the last decade, recording the main electricity power consumed in individual households. This data produces valuable information that can help them reduce their electricity footprint; nevertheless, the collected signal aggregates the consumption of the different appliances running simultaneously in the house, making it difficult to apprehend. Non-Intrusive Load Monitoring (NILM) refers to the challenge of estimating the power consumption, pattern, or on/off state activation of individual appliances using the main smart meter signal. Recent methods proposed to tackle this task are based on a fully supervised deep-learning approach that requires both the aggregate signal and the ground truth of individual appliance power. However, such labels are expensive to collect and extremely scarce in practice, as they require conducting intrusive surveys in households to monitor each appliance. In this paper, we introduce CamAL, a weakly supervised approach for appliance pattern localization that only requires information on the presence of an appliance in a household to be trained. CamAL merges an ensemble of deep-learning classifiers combined with an explainable classification method to be able to localize appliance patterns. Our experimental evaluation, conducted on 4 real-world datasets, demonstrates that CamAL significantly outperforms existing weakly supervised baselines and that current SotA fully supervised NILM approaches require significantly more labels to reach CamAL performances. The source of our experiments is available at: https://github.com/adrienpetralia/CamAL
Normal form analysis of nonlinear oscillator equations with automated arbitrary order expansions
No full textInternational audienceArbitrary order expansions for the automatic reduction and solutions of nonlinear vibratory systems have been developed successfully within the realm of the direct parametrisation of invariant manifolds. Whereas the method has been used with high-order expansions and large dimensional systems, this article proposes to look at the same problem from the opposite point of view. By using low-dimensionalsystems, symbolic computations, analytical developments and numerical verifications, this contribution analyses the reduced dynamics appearing in cases where a single master mode is involved, reviewing typical scenarios in nonlinear vibrations: primary resonance, sub- and superharmonic resonances and parametric excitation. To achieve this task, the normal form style is preferentially used. A symbolic open-source package is also provided to generalise the presented results to other styles, higher orders, and different scenarios. It is shown how the low-order terms allow recovering the classical solutions given by perturbation methods, and how the automated expansions allow one to generalise the analysis to arbitrary orders. When analytical solutions are not tractable anymore, numerical solutions are employed to underline how converged solutions are at hand when the validity limit of the expansions is not reached. All the results presented in this paper can thus be used to better understand the nonlinear dynamical solutions occurring in nonlinear vibrations, as well as from a system identification perspective, since the normal form is the simplest dynamical system displaying a given resonance scenario
Population exposure to outdoor NO2, black carbon, and ultrafine and fine particles over Paris with multi-scale modelling down to the street scale
No full textInternational audienceThis study focuses on mapping the concentrations of pollutants of interest to health (NO2, black carbon (BC), PM2.5, and particle number concentration (PNC)) down to the street scale to represent the population exposure to outdoor concentrations at residences. Simulations are performed over the area of Greater Paris with the WRF-CHIMERE/MUNICH/SSH-aerosol chain, using either the top-down inventory EMEP or the bottom-up inventory Airparif, with correction of the traffic flow. The concentrations of the pollutants are higher in streets than in the regional-scale urban background, due to the strong influence of road traffic emissions locally. Model-to-observation comparisons were performed at urban background and traffic stations and evaluated using two performance criteria from the literature. For BC, harmonized equivalent BC (eBC) concentrations were estimated from concomitant measurements of eBC and elemental carbon. Using the bottom-up inventory with corrected road traffic flow, the strictest criteria are met for NO2, eBC, PM2.5, and PNC. Using the EMEP top-down inventory, the strictest criteria are also met for NO2, eBC, and PM2.5, but errors tend to be larger than with the bottom-up inventory for NO2, eBC, and PNC. Using the top-down inventory, the concentrations tend to be lower along the streets than those simulated using the bottom-up inventory, especially for NO2 concentrations, resulting in fewer urban heterogeneities. The impact of the size distribution of non-exhaust emissions was analysed at both regional and local scales, and it is higher in heavy-traffic streets. To assess exposure, a French database detailing the number of inhabitants in each building was used. The population-weighted concentration (PWC) was calculated by weighting populations by the outdoor concentrations to which they are exposed at the precise location of their home. An exposure scaling factor (ESF) was determined for each pollutant to estimate the ratio needed to correct urban background concentrations in order to assess exposure. The average ESF in Paris and the Paris ring road is higher than 1 for NO2, eBC, PM2.5, and PNC because the concentrations simulated at the local scale in streets are higher than those modelled at the regional scale. It indicates that the Parisian population exposure is underestimated using regional-scale concentrations. Although this underestimation is low for PM2.5, with an ESF of 1.04, it is very high for NO2 (1.26), eBC (between 1.22 and 1.24), and PNC (1.12). This shows that urban heterogeneities are important to be considered in order to represent the population exposure to NO2, eBC, and PNC but less so for PM2.5