Study introduces a probabilistic framework for air-sea fluxes using neural networks.
problem Accurately quantifying air-sea fluxes for understanding interactions and improving weather/climate models.
method Gaussian distributions conditioned on input variables, artificial neural networks, eddy-covariance data, minimizing negative log-likelihood loss.
result Trained neural networks provide alternative mean flux estimates and quantify uncertainty.
Neural surrogates speed up 5D gyrokinetic simulations of plasma turbulence.
problem Expensive numerical simulations of plasma turbulence hinder fusion reactor design.
method Trained a hierarchical vision transformer in 5D to predict plasma quantities faster.
result Neural surrogates predict plasma quantities two orders of magnitude faster than numerical codes.
GyroSwin models plasma turbulence with neural nets, reducing costs and capturing neglected nonlinearities.
problem Understanding plasma turbulence in fusion reactors, which impairs confinement and limits reactor design.
method Introduces GyroSwin, a scalable 5D neural surrogate that approximates 5D nonlinear gyrokinetic simulations.
result GyroSwin outperforms reduced models in heat flux prediction and captures turbulent energy cascade.
We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network (NN) schemes trained on Direct Numerical Simulations of turbulence. Even the bare DL solutions, which do not take into account any physics …
Deep learning compares turbulence models in plasma physics.
problem Predicting edge plasma turbulence in magnetic fusion reactors.
method Physics-informed deep learning framework for comparing two-fluid and gyrokinetic models.
result Good overall agreement between two-fluid theory and gyrokinetic models in turbulent field fluctuations.
Metabolic flux balance analyses are a standard tool in analysing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear prog…
Study on complex tori foliations and flat geometries.
problem Understanding turbulent foliations on compact complex tori.
method Defined and analyzed smooth turbulent foliations on compact complex tori.
result All transversely holomorphic Cartan geometries are flat.
NN-Turb generates turbulent velocity statistics using neural networks.
problem Creating a 1D field with turbulent velocity statistics.
method Fully-convolutional neural network (NN-Turb) to generate the field.
result NN-Turb generates a 1D field that satisfies Kolmogorov's 2/3 and 4/5 laws, exhibiting intermittency.
Transfer learning improves chaotic dynamics predictions with less data.
problem Efficiently predicting chaotic dynamics with limited data.
method Transfer learning for nonlinear dynamics, optimizing transfer rate and leveraging small-scale turbulence universality.
result Significantly more accurate inference of chaotic dynamics achieved.
Physics-informed ML models improve turbulence understanding in fusion plasmas.
problem Improving turbulence modeling in fusion plasma devices.
method Physics-informed deep learning framework constrained by PDEs.
result Direct quantitative comparisons of turbulent fields between theory and gyrokinetic models.
Neural networks predict flow and elastic stresses in viscoelastic turbulence.
problem Predicting flow and elastic stresses in viscoelastic turbulent flows using limited experimental data.
method Convolutional neural networks trained on wall-normal velocity and pressure data.
result Neural networks accurately predict flow and elastic stresses, especially during low-drag events.
While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim to predict turbulent flow by learning its highly nonlinear dynamics from spatio…
xVAE models extreme turbulence events in turbulent flows.
problem Capturing extreme events in turbulent flows.
method Max-infinitely divisible process with heavy-tailed distributions embedded into a standard VAE framework.
result xVAE more robust in capturing extreme values compared to POD modes.
Researchers use quantum chaos and RMT to analyze turbulence, revealing unique scaling laws.
problem Understanding the statistical structure and scaling laws of turbulence.
method Applied tools from quantum chaos and Random Matrix Theory to analyze turbulence datasets.
result Turbulence Gram matrices exhibit power-law scalings distinct from classical chaos and random data.
Study predicts turbulent electric fields in fusion plasmas using deep learning.
problem Predicting turbulent electric fields in fusion plasmas.
method Physics-informed deep learning, drift-reduced Braginskii theory, experimental data.
result Neutrals broaden turbulent field amplitudes and increase shearing rates.
Neural network predicts turbulence near-wall regions efficiently.
problem Reducing computational cost in turbulent flow simulations.
method Fully-convolutional neural network trained on DNS data.
result FCN predicts velocity fluctuations at y+=50 with less than 20% error. The understanding of the dynamics of the velocity gradients in turbulent flows is critical to understanding various non-linear turbulent processes. The pressure-Hessian and the viscous-Laplacian govern the evolution of the velocity-gradients and are known to be non-local in nature. Over the years, several simplified dy…
11D supergravity completes with quantized C-field flux.
problem Completing 11D supergravity with quantized C-field flux.
method Duality-symmetric formulation of on-shell 11d supergravity on superspace.
result 11d super-spacetimes are quantizable by duality-symmetric super-C-field flux.
We study the flux homomorphism for closed forms of arbitrary degree, with special emphasis on volume forms and on symplectic forms. The volume flux group is an invariant of the underlying manifold, whose non-vanishing implies that the manifold resembles one with a circle action with homologically essential orbits.
Analyzes quantization of flux observables in gauge theories.
problem Lifting Poisson-brackets of flux observables to higher moduli stacks.
method Systematic analysis of canonical quantization and flux quantization laws.
result Topological quantum observables form homology Pontrjagin algebra of loop space.
Three physics-constrained regression exercises for image velocimetry and turbulence modeling.
problem Image velocimetry and turbulence modeling challenges.
method Physics-constrained regression exercises implemented as toy problems.
result Python codes provided for all exercises.
M5-branes' flux quantization linked to non-abelian cohomology.
problem Flux quantization on M5-branes and its implications.
method Analogous to Dirac's charge/flux quantization, constraining M5's flux-quantization law to non-abelian cohomology theory.
result Skyrmion-like and anyonic solitons on M5-branes and open M5-branes.
Study on Euler class and flux homomorphisms for non-orientable surfaces.
problem Investigate Euler class and flux homomorphisms for non-orientable surfaces.
method Analyze Euler class and flux homomorphisms for non-orientable compact surfaces with one boundary component.
result Prove the simplicity of the kernel of the flux homomorphisms, implying the non-existence of invariants analogous to the Calabi invariant.
It is known that the topological T-duality exchanges H and F-fluxes. In this paper, we reformulate the topological T-duality as an exchange of two Lie algebroids in the generalized tangent bundle. Then, we apply the same formulation to the Poisson-generalized geometry, which is introduced in arXiv:1408.2649 to defi…
No projective structure found on foliations of elliptic curves.
problem Existence of transversely projective structures on foliations.
method Analyzing foliations on the product of two elliptic curves.
result Generic nonsingular turbulent foliations do not have transversely projective structures.
New turbulence index using TDA detects financial market transitions.
problem Detecting critical transitions in financial markets.
method Persistent homology for identifying topological features.
result Persistent homology-based index captures financial data transitions.
The paper analyzes financial market turbulence using mathematical physics.
problem Understanding price fluctuations caused by information asymmetry.
method Spectrum analysis to decompose pricing patterns.
result Identifies phase correlations in financial stock market turbulence.
Convolutional networks predict turbulence from wall quantities.
problem Predicting turbulence fields from wall-shear-stress components and wall pressure.
method Two CNN models: FCN and FCN-POD, trained on DNS data.
result FCN and FCN-POD models outperform EPOD in predicting turbulence fields.
We give a systematic derivation of the local expressions of the NS H-flux, geometric F- as well as non-geometric Q- and R-fluxes in terms of bivector beta- and two-form B-potentials including vielbeins. They are obtained using a supergeometric method on QP-manifolds by twist of the standard Courant algebroid on the gen…
FLUXtrapolation benchmarks machine learning for extrapolating ecosystem fluxes under distribution shifts.
problem Machine learning challenges in extrapolating ecosystem fluxes under distribution shifts.
method Defined temporal, spatial, and temperature-based extrapolation scenarios; evaluated performance across domains, temporal aggregations, and tail errors.
result Baselines perform similarly under median hourly RMSE but differ under tail-focused and multi-scale evaluations.
Combines machine learning and convex limiting for accurate subgrid flux modeling in shallow-water equations.
problem Accurate subgrid flux modeling in shallow-water equations.
method Machine learning and flux limiting for property-preserving subgrid scale modeling.
result The proposed method produces meaningful closures even in untrained scenarios.
Physics-guided reinforcement learning optimizes swimming in turbulent flows.
problem Optimizing swimming efforts to maintain proximity in turbulent environments.
method Physics-informed actor-physicist reinforcement learning algorithm.
result Physics-informed reinforcement learning outperforms standard methods in turbulent flow control.
This paper examines cryptocurrency integration with traditional markets, showing how network structure and turbulence influence cross-asset spillovers.
problem Understanding how cryptocurrencies integrate with traditional financial markets and the impact of market stress on cross-asset spillovers.
method Combining rolling correlation networks, community structure, market-specific and system-wide Turbulence Indices, and VAR-based connectedness analysis.
result Cross-asset integration is episodic, with network structure and turbulence playing a role in transmission during stress periods.
We compute the flux of Killing fields through ends of constant mean curvature 1 in hyperbolic space, and we prove a result conjectured by Rossman, Umehara and Yamada : the flux matrix they have defined is equivalent to the flux of Killing fields. We next give a geometric description of embedded ends of finite total cur…
HFNO enhances interpretability of turbulent flows through parallel wavenumber bin processing.
problem Opaque inner workings of Fourier Neural Operators (FNOs) hinder physical interpretability.
method Introduces HFNO, a novel FNO-based architecture that processes wavenumber bins in parallel, enhancing interpretability.
result HFNO decomposes turbulent flows across various scales, enabling increased interpretability and multiscale modeling.
New correspondence links fluxless to fluxy flag manifolds via T-duality.
problem Understanding fluxes on flag manifolds.
method Defining a new correspondence and using infinitesimal T-duality.
result Infinitesimal T-duality generates nontrivial fluxes.
Starting from a higher Courant bracket associated to exceptional generalized geometry, we provide a systematic derivation of all types of fluxes and their Bianchi identities for four-dimensional compactifications of M-theory. We show that these fluxes may be understood as generalized Wess-Zumino terms in certain topolo…
New method for flux quantization on phase space stacks.
problem Defining and constructing flux-quantized phase space stacks.
method Observation of flux densities and characterization of Cauchy data.
result Flux-quantized phase space stacks have classifying spaces with rational Whitehead L-infinity algebra.
Paper studies flows of spinor fields with flux for unified theories.
problem Existence of covariantly constant spinors in unified theories.
method Introduces parabolic flows of spinor fields to find stationary points.
result Establishes short-time existence and smoothing estimates for spinor flows.
Machine learning and deep learning infer surface/groundwater exchange from temperature data.
problem Inferring surface/groundwater exchange from temperature data with high temporal resolution.
method Application of machine learning and deep learning algorithms to infer surface/groundwater exchange flux from subsurface temperature observations.
result DL methods outperform ML methods in interpreting noisy temperature data, especially with a smoothing filter.
Modernizes higher-dimensional supergravity, linking it to flux quantization.
problem Constructing infrared completions of higher-dimensional supergravity.
method Using differential nonabelian cohomology and super-torsion constraints.
result Equivalence of solutions in different dimensions and flux quantization.
Invariant r♯ predicts H-flux behavior under T-duality.
problem Predicting H-flux behavior under T-duality on product manifolds.
method Using r♯ invariant to analyze metric connections and T-duality effects. result Invariant r♯ detects irreducible H-flux components that survive T-duality. TURB-Rot provides a large database of turbulent rotating flow snapshots for research.
problem Lack of large-scale, high-resolution datasets for turbulent rotating flows.
method Direct Numerical Simulations of Navier-Stokes equations with rotation.
result Provides a diverse set of 300K complex images and fields for testing.
On a closed symplectic surface Sigma of genus two or more, we give a new construction of an extended flux map (a crossed homomorphism from the symplectomorphism group Symp(Sigma) to the cohomology group H^1(Sigma;R) that extends the flux homomorphism). This construction uses the topology of the Jacobian of the surface …
New mathematical framework connects M-theory charges to stable homotopy groups.
problem Quantization of fluxes in M-theory and their mathematical representation.
method Establishing a correspondence between M-theory phenomena and stable homotopy theory concepts.
result Found a direct link between M-theory charges and stable homotopy groups.
A novel gravity theory based on Poisson Generalized Geometry is investigated. A gravity theory on a Poisson manifold equipped with a Riemannian metric is constructed from a contravariant version of the Levi-Civita connection, which is based on the Lie algebroid of a Poisson manifold. Then, we show that in Poisson Gener…
For a complete minimal surface in the Euclidean 3-space, the so-called flux vector corresponds to each end. The flux vectors are balanced, i.e., the sum of those over all ends are zero. Consider the following inverse problem: For each balanced n vectors, find an n-end catenoid which attains given vectors as flux. Here,…
Study on measurable pseudo-Anosov maps on surfaces.
problem Characterize dynamics of pseudo-Anosov maps on surfaces.
method Analyze measurable pseudo-Anosov homeomorphisms with specific properties.
result Prove transitivity, dense periodic points, sensitivity, and ergodicity.