HAL-Polytechnique
Not a member yet
51406 research outputs found
Sort by
Defect Suppression and Efficiency Enhancement of Slot-Die-Coated Formamidinium Lead Iodide Solar Devices via Ambient Air Annealing
No full textInternational audienc
Continuous microstructure variations with graded properties in directed energy deposition
No full textInternational audienceDirected energy deposition additive manufacturing is a versatile technique for fabricating complex geometries, where precise control of process parameters is crucial for tailoring microstructure and part properties. Microstructure control strategies usually involve variation of material composition (i.e., functionally graded materials) or interlayer time delay. However, the obtained microstructures are usually uniform in the print direction and exhibit sharp transitions from one layer to the next in the build direction. This paper targets continuous microstructural variation by exploiting active cooling strategies to control cooling conditions. To do so, the scanning speed is continuously varied, necessitating accommodating the bead size variations with non-standard trajectory generation based on a phenomenological law. The proposed strategy is demonstrated on thin-wall structures made of IN718 using a powder-based laser directed energy deposition. The results reveal a continuous microstructural transition along the print direction, characterized by two distinct microstructural regimes with markedly different morphological features and crystallographic textures. This demonstrates the capability of scanning speed modulation to engineer heterogeneous microstructures within a single component, offering insights into tailoring material properties for specific engineering applications.</div
The Gerontocracy of Science: Attention Dynamics in the Age
No full text19 pages, 7 figuresScientific literature has been growing exponentially for decades, with publications from the last twenty years now comprising 60% of all academic output. While the impact of information overload on news and social-media consumption is well-documented, its consequences on scientific progress remain understudied. Here, we investigate how this rapid expansion affects the circulation and exploitation of scientific ideas. Unlike other cultural domains, science is experiencing a decline in the proportion of highly influential papers and a slower turnover in its canons. This results in the disproportionate persistence of established works, a phenomenon we term the ``gerontocratization of science''. To test whether hypergrowth drives this trend, we develop a generative citation model that incorporates random discovery, cumulative advantage, and exponential growth of the scientific literature. Our findings reveal that as scientific output expands exponentially, gerontocratization emerges and intensifies, reducing the influence of new research. Recognizing and understanding this mechanism is crucial for developing targeted strategies to sustain intellectual dynamism and ensure a balanced and healthy renewal of scientific knowledge
Quadratization of Autonomous Partial Differential Equations: Theory and Algorithms
No full textQuadratization for partial differential equations (PDEs) is a process that transforms a nonquadratic PDE into a quadratic form by introducing auxiliary variables. This symbolic transformation has been used in diverse fields to simplify the analysis, simulation, and control of nonlinear and nonquadratic PDE models. This paper presents a rigorous definition of PDE quadratization, theoretical results for the PDE quadratization problem of spatially one-dimensional PDEs-including results on existence and complexity-and introduces QuPDE, an algorithm based on symbolic computation and discrete optimization that outputs a quadratization for any spatially one-dimensional polynomial or rational PDE. This algorithm is the first computational tool to find quadratizations for PDEs to date. We demonstrate QuPDE's performance by applying it to fourteen nonquadratic PDEs in diverse areas such as fluid mechanics, space physics, chemical engineering, and biological processes. QuPDE delivers a low-order quadratization in each case, uncovering quadratic transformations with fewer auxiliary variables than those previously discovered in the literature for some examples, and finding quadratizations for systems that had not been transformed to quadratic form before
On the simulation of extreme events with neural networks
No full textInternational audienceThis article aims at investigating the use of generative methods based on neural networks to simulate extreme events. Although very popular, these methods are mainly invoked in empirical works. Therefore, providing theoretical guidelines for using such models in extreme values context is of primal importance. To this end, we propose an overview of most recent generative methods dedicated to extremes, giving some theoretical and practical tips on their tail behaviour thanks to both extreme-value and copula tools
Observation of emergent scaling of spin–charge correlations at the onset of the pseudogap
No full textInternational audienceIn strongly correlated materials, interacting electrons are entangled and form collective quantum states, resulting in rich low-temperature phase diagrams. Notable examples include cuprate superconductors, in which superconductivity emerges at low doping out of an unusual “pseudogap” metallic state above the critical temperature. The Fermi–Hubbard model, describing a wide range of phenomena associated with strong electron correlations, still offers major computational challenges despite its simple formulation. In this context, ultracold atoms quantum simulators have provided invaluable insights into the microscopic nature of correlated quantum states. Here, we use a quantum gas microscope Fermi–Hubbard simulator to explore a wide range of dopings and temperatures in a regime where a pseudogap is known to develop. By measuring multipoint correlation functions up to fifth order, we uncover a universal scaling behavior in magnetic and higher-order spin–charge correlations characterized by a doping-dependent temperature scale. Accurate comparisons with determinant Quantum Monte Carlo and Minimally Entangled Typical Thermal States simulations confirm that this temperature scale is comparable to the pseudogap temperature T ∗ . Our quantitative findings reveal a qualitative behavior of magnetic properties and spin–charge correlations in an emergent pseudogap and pave the way toward the exploration of charge pairing and collective phenomena expected at lower temperatures
Two-Temperature and Thermal Plasma Kinetic Theories
No full textWe first review a two-temperature kinetic theory of multicomponent magnetized reactive plasmas where electrons and heavy species have their own temperature. The Knudsen number is taken to be proportional to the square root of the mass ratio and polyatomic species are taken into account.We then review the one-temperature kinetic theory of multicomponent magnetized reactive plasmas when the mass ratio remains of order unity. The complex tensorial structure of the transport fluxes is addressed as well as the symmetry properties of the multicomponent transport coefficients. We then establish new links between these two theories by using the two-temperature scaling in the transport linear system obtained from the one-temperature kinetic theory. The flux structure of the two-temperature theory is recovered from the equilibrium theory as well as the second order corrector terms. We also address the solution of transport linear systems by using fast and convergent iterative algorithms and their improvement for ionized mixtures
New insight on the global dynamics in the "transition region" of Venus atmosphere (80-130 km) with a 3D model
No full textInternational audienceVenus’ atmosphere layers between 80 km and 130 km mark the transition between the superrotation and the day-to-night circulation regimes. Accurately modeling this layer is essential to better understand the planet’s atmospheric dynamics. However, this re-gion remains poorly constrained by observations, and its variability is not yet fully captured by current 3D models. Here we use the latest version of the Venus Planetary Climate Model (V-PCM), a ground-to-thermosphere global circulation model, to investigate possible scenarios relevant to future EnVision observations above the cloud tops. We focus on current data-model biases and provide a tentative interpretation of their origin. Benchmark simulations by Martinez et al. (2024) overestimate the nightside O airglow emission by a factor of two and place the emission peak 5–7 km higher than observed. Furthermore, the emission distribution is not centered around midnight, but shifted to LT=4h, likely due to a strong (∼100 m/s) zonal wind component below 105 km. We performed sensitivity tests on unconstrained parameters (e.g. gravity wave drag and eddy diffusion) to evaluate their impact on the dynamical structures. Results show that reducing non-orographic gravity wave forcing below 105 km weakens that superrotation wind component, and recenter the emission around midnight. However, the altitude bias appears linked to insufficient vertical transport in the model. These findings underline the need for future space missions capable of continuously monitoring mesospheric gravity waves and O2 nightglow to better constrain their spatial and temporal variability and improve the representation of key dynamical processes in Venus’ upper atmosphere
Tunable spin-crossover in 2D ruthenium metal–organic frameworks based on hexahydroxybenzene ligand
No full textInternational audienc
Data Valuation for LLM Fine-Tuning: Efficient Shapley Value Approximation via Language Model Arithmetic
No full text11 pages, 2 figuresData is a critical asset for training large language models (LLMs), alongside compute resources and skilled workers. While sometraining data is publicly available, substantial investment is required to generate proprietary datasets, such as human preferenceannotations or to curate new ones from existing sources. As larger datasets generally yield better model performance, two naturalquestions arise. First, how can data owners make informed decisions about curation strategies and data sources investment? Second,how can multiple data owners collaboratively pool their resources to train superior models while fairly distributing the benefits?This problem, data valuation, which is not specific to large language models, has been addressed by the machine learning communitythrough the lens of cooperative game theory, with the Shapley value being the prevalent solution concept. However, computing Shapleyvalues is notoriously expensive for data valuation, typically requiring numerous model retrainings, which can become prohibitive forlarge machine learning models. In this work, we demonstrate that this computational challenge is dramatically simplified for LLMstrained with Direct Preference Optimization (DPO). We show how the specific mathematical structure of DPO enables scalable Shapleyvalue computation. We believe this observation unlocks many applications at the intersection of data valuation and large languagemodels