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Modelling Thomson scattering in a hydrogen plasma at stellar interior conditions using the hypernetted‐chain approach
No full textInternational audienceUnder the extreme conditions found in small stars, where electron degeneracy and Coulomb coupling are significant, accurate modeling of Thomson scattering is crucial for determining opacity, a primary quantity for stellar energy transport. We use hypernetted‐chain calculations, incorporating quantum pseudopotentials and electron‐exchange effects to obtain the electron–electron static structure factor to calculate the Thomson scattering transport cross‐section for conditions prevailing in the interior of small stars. These results are compared to those from average‐atom simulations and analytical calculations. Our findings support laboratory astrophysics experiments aimed at benchmarking opacity models for stellar interiors, particularly for red dwarf stars, and help to bridge theoretical models with observations
Fast modular composition using spiroids
No full textGiven three univariate polynomials P, Q, and R with coefficients in a prime finite field, we present a new algorithm for computing P ∘ Q modulo R in time close to linear and with an asymptotic complexity smaller than the one of the Kedlaya-Umans algorithm. As a novelty, our method mostly performs fast floating point Fourier transforms, while previously known ones rely on ad hoc algebraic constructions of finite fields
Observed Circulation Trends in Boreal Summer Linked to Two Spatially Distinct Teleconnection Patterns
No full textInternational audienceVarious regions in the Northern Hemisphere midlatitudes have seen pronounced trends in upperatmosphere summer circulation and surface temperature extremes over recent decades (since 1979). Several of these regional trends lie outside the range of historic CMIP6 model simulations, and they might constitute a joined dynamic response that is missed by climate models. Here, we examine if the regional trends in circulation are indeed part of a coherent circumglobal wave pattern. Using ERA5 reanalysis data and CMIP6 historical simulations, we find that the observed upper-atmospheric circulation trends consist of at least two separate regional signatures: a U.S.-Atlantic pattern and a Eurasian trend pattern. The circulation trend can explain on average 15% and 26% of the observed regional temperature trends in the U.S.-Atlantic and Eurasian regions, respectively. The circulation trend in the CMIP6 multimodel mean does not resemble the observed trend pattern and is much weaker overall. Some individual CMIP6 models do show a resemblance to the observed pattern in ERA5, although still weak with a maximum pattern correlation of 0.47. The pattern correlation is higher for the two individual regions (U.S.-Atlantic and Eurasia), reaching a maximum of 0.69 and 0.78. We show that both regional wave patterns in ERA5 are associated with distinct sea surface temperature and outgoing longwave radiation anomalies in the 3 weeks leading up to the atmospheric configuration, resembling known teleconnection patterns. CMIP6 models appear to lack these tropical-extratropical teleconnections. Our findings highlight the limitations of CMIP6 models in reproducing teleconnections and their associated regional imprint, creating deep uncertainty for regional climate projections on decadal-to-multidecadal time scales
Efficient Simulation of Hawkes Processes using their Affine Volterra Structure
No full textWe introduce a novel and efficient simulation scheme for Hawkes processes on a fixed time grid, leveraging their affine Volterra structure. The key idea is to first simulate the integrated intensity and the counting process using Inverse Gaussian and Poisson distributions, from which the jump times can then be easily recovered. Unlike conventional exact algorithms based on sampling jump times first, which have random computational complexity and can be prohibitive in the presence of high activity or singular kernels, our scheme has deterministic complexity which enables efficient large-scale Monte Carlo simulations and facilitates vectorization. Our method applies to any nonnegative, locally integrable kernel, including singular and non-monotone ones. By reformulating the scheme as a stochastic Volterra equation with a measure-valued kernel, we establish weak convergence to the target Hawkes process in the Skorokhod J1topology. Numerical experiments confirm substantial computational gains while preserving high accuracy across a wide range of kernels, with remarkably improved performance for a variant of our scheme based on the resolvent of the kernel
Comparing Symmetrized Determinant Neural Quantum States for the Hubbard Model
No full textAccurate simulations of the Hubbard model are crucial to understanding strongly correlated phenomena, where small energy differences between competing orders demand high numerical precision. In this work, Neural Quantum States are used to probe the strongly coupled and underdoped regime of the square-lattice Hubbard model. We systematically compare the Hidden Fermion Determinant State and the Jastrow-Backflow ansatz, parametrized by a Vision Transformer, finding that in practice, their accuracy is similar. We also test different symmetrization strategies, finding that output averaging yields the lowest energies, though it becomes costly for larger system sizes. On cylindrical systems, we consistently observe filled stripes. On the torus, our calculations display features consistent with a doped Mott insulator, including antiferromagnetic correlations and suppressed density fluctuations. Our results demonstrate both the promise and current challenges of neural quantum states for correlated fermions
Biomechanical finite element simulation of the pelvic organs under dynamic loading and validation against experimental data from magnetic resonance imaging
No full textInternational audiencePelvic organ prolapse (POP) is a prevalent condition affecting women, particularly those over the age of 50. The etiology and pathophysiology of this condition remain poorly understood within the medical community. In recent years, researchers, particularly medical engineers and biomechanical scientists, have initiated studies on this female pathology. Numerous finite element analyses have been conducted to determine the material properties of tissues involved in POP. Building on the material properties established in prior research, this study presents a patient-specific model derived from patient-specific MRI data. Intra-abdominal pressure (IAP) and boundary conditions were determined from MRI analysis, and the models were validated against MRI simulations encompassing 11 seconds with a 1-second step interval. This study compares the outcomes of our models with MRI results, providing insights into POP biomechanics. A good correlation was observed between MRI data and the finite element method (FEM) models in healthy patients, particularly for the bladder when fluid properties, such as urine, were included. A relative error between 18% and 26% was observed for bladder displacement. Moreover, the models provided acceptable results for the uterus, vagina, and rectum. Visual results supporting these findings are presented in this study
Comparative Study on Microwave Diagnostics: Cutoff Probe, Hairpin Probe, and Microwave Interferometer
No full textInternational audienceWe have compared three microwave diagnostic techniques for electron density measurement in a 13.56MHz ICP reactor in pure Ar: the cutoff probe, hairpin probe, and a microwave interferometer. The interferometer showed the highest density, followed by the hairpin and cutoff probes, indicating that the probe antenna surface acts as a loss channel for charged particles, leading to a reduction in electron density. Furthermore, we found that the perturbation is localized near the probe
Nonparametric intensity estimation of spatial point processes by random forests
No full textWe propose a random forest estimator for the intensity of spatial point processes, applicable with or without covariates. It retains the well-known advantages of a random forest approach, including the ability to handle a large number of covariates, out-of-bag cross-validation, and variable importance assessment. Importantly, even in the absence of covariates, it requires no border correction and adapts naturally to irregularly shaped domains and manifolds. Consistency and convergence rates are established under various asymptotic regimes, revealing the benefit of using covariates when available. Numerical experiments illustrate the methodology and demonstrate that it performs competitively with state-of-the-art methods
Proof Compression via Subatomic Logic and Guarded Substitutions
No full textInternational audienceSubatomic logic is a recent innovation in structural proof theory where atoms are no longer the smallest entity in a logical formula, but are instead treated as binary connectives. As a consequence, we can give a subatomic proof system for propositional classical logic such that all derivations are strictly linear: no inference step deletes or adds information, even units. In this paper, we introduce a powerful new proof compression mechanism that we call guarded substitutions, a variant of explicit substitutions, which substitute only guarded occurrences of a free variable, instead of all free occurrences. This allows us to construct "superpositions" of derivations, which simultaneously represent multiple subderivations. We show that a subatomic proof system with guarded substitution can p-simulate a Frege system with substitution, and moreover, the cut-rule is not required to do so.</div
Deep learning in the abyss: a stratified Physics Informed Neural Network for data assimilation
No full textThe reconstruction of deep ocean currents is a major challenge in data assimilation due to the scarcity of interior data. In this work, we present a proof of concept for deep ocean flow reconstruction using a Physics-Informed Neural Network (PINN), a machine learning approach that offers an alternative to traditional data assimilation methods. We introduce an efficient algorithm called StrAssPINN (for Stratified Assimilation PINNs), which assigns a separate network to each layer of the ocean model while allowing them to interact during training. The neural network takes spatiotemporal coordinates as input and predicts the velocity field at those points. Using a SIREN architecture (a multilayer perceptron with sine activation functions), which has proven effective in various contexts, the network is trained using both available observational data and dynamical priors enforced at several collocation points. We apply this method to pseudo-observed ocean data generated from a 3-layer quasi-geostrophic model, where the pseudo-observations include surface-level data akin to SWOT observations of sea surface height, interior data similar to ARGO floats, and a limited number of deep ARGO-like measurements in the lower layers. Our approach successfully reconstructs ocean flows in both the interior and surface layers, demonstrating a strong ability to resolve key ocean mesoscale features, including vortex rings, eastward jets associated with potential vorticity fronts, and smoother Rossby waves. This work serves as a prelude to applying StrAssPINN to real-world observational data