arXiv research

RFM improves CNFs by adding a boundary constraint term and matching velocity fields.

CCVFM uses coreset to improve generative models by refining residual flows.

We introduce a measure for estimating the best risk-return relation of power production in wind farms within a given time-lag, conditioned to the velocity field. The velocity field is represented by a scalar that weighs the influence of the velocity at each wind turbine at present and previous time-steps for the presen…

Generative sampler learns velocity fields for efficient posterior inference.

SAGE generates subsurface velocity models from sparse well logs and seismic images.

A conservative drifting method improves generative modeling by using KDE gradients, proving convergence rates.

MeanFlow training is unstable due to misusing conditional velocity, leading to variance issues.

New method estimates velocity fields for minimizing $f$-divergences without overfitting.

Generative models on spheres improve discrete sequence sampling.

New method distinguishes cause from effect using causal velocity.

A perfect-fluid space-time of dimension n>3 with 1) irrotational velocity vector field, 2) null divergence of the Weyl tensor, is a generalised Robertson-Walker space-time with Einstein fiber. Condition 1) is verified whenever pressure and energy density are related by an equation of state. The contraction of the Weyl …

New optimality conditions for sub-Riemannian geodesics derived.

Proves no-hair theorem for certain vacuum black holes.

We discuss some aspects about the computation of kinematic, spectroscopic, Fermi and astrometric relative velocities that are geometrically defined in general relativity. Mainly, we state that kinematic and spectroscopic relative velocities only depend on the 4-velocities of the observer and the test particle, unlike F…

A theorem proves a surface evolution graph satisfies a PDE under specific conditions.

DSNE visualizes data velocity in lower dimensions.

Paper solves Serrin problem for ring-shaped domains, showing velocity has finitely many maxima.

We propose a predictive neural network architecture that can be utilized to update reference velocity models as inputs to the full waveform inversion. Deep learning models are explored to augment velocity model building workflows during processing the 3D seismic volume in salt-prone environments. Specifically, a neural…

Log-ergodic model improves velocity of money prediction.

By further developing the generalized $Γ$-calculus for hypoelliptic operators, we prove hypocoercive estimates for a large class of Kolmogorov type operators which are defined on non necessarily totally geodesic Riemannian foliations. We study then in detail the example of the velocity spherical Brownian motion, whose …

Improved efficiency in HMC samplers reduces dissipative behavior.

Optimal self-distillation improves generative models' velocity risk and mode recovery.

Deep learning predicts fluid flow in porous media, accelerating simulations by orders of magnitude.

New conditions prevent gaps in optimal control problems.

NGBoost boosts multivariate probabilistic regression.

We prove that, in a space-time of dimension n>3 with a velocity field that is shear-free, vorticity-free and acceleration-free, the covariant divergence of the Weyl tensor is zero if the contraction of the Weyl tensor with the velocity is zero. The other way, if the covariant divergence of the Weyl tensor is zero, then…

Proves strong solutions for graphical Brakke flows with $L^2$ normal velocity.

In this paper the authors study the hyperbolic geometric flow on Riemann surfaces. This new nonlinear geometric evolution equation was recently introduced by the first two authors motivated by Einstein equation and Hamilton's Ricci flow. We prove that, for any given initial metric on ${\mathbb{R}}^{2}$ in certain class…

Develops a method for non-equilibrium importance sampling to estimate expectations and constants.

Novel alignment framework for text-to-image generation using diffusion models and flow matching.

LFIS uses a time-dependent velocity field to sample from complex distributions.

NN-Turb generates turbulent velocity statistics using neural networks.

Bitcoin's monetary velocity is constrained by network friction, leading to significant utility contraction during shocks.

The determinants of the velocity of money have been examined based on life-cycle hypothesis. The velocity of money can be expressed by reciprocal of the average value of holding time which is defined as interval between participating exchanges for one unit of money. This expression indicates that the velocity is govern…

LFlows model fluid densities and velocities using invertible maps that satisfy the continuity equation.

Paper revises power theory using classical mechanics concepts.

A new method interprets astrophysical spectra using geometric paths to distinguish line profiles.

Neural solver computes Wasserstein geodesics and velocity fields efficiently.

Develops scalable model for learning velocity fields in complex traffic scenarios.

We reconsider the problem of optimal trading in the presence of linear and quadratic costs, for arbitrary linear costs but in the limit where quadratic costs are small. Using matched asymptotic expansion techniques, we find that the trading speed vanishes inside a band that is narrower than in the absence of quadratic …

Time dilation $\frac{1}{\sqrt{1-v^2}}$ and relative velocity $v$ are observationally indistinguishable in the special theory of relativity, a duality that carries over into the general theory under Fermi coordinates along a curve (in coordinate-independent language, in the tangent Minkowski space along the curve). For …

An $(r,n)$-velocity is an $r$-jet with source at $0 \in \R^n$, and target in a manifold $Y$. An $(r,n)$-velocity is said to be regular, if it has a representative which is an immersion at $0 \in \R^{n}$. The manifold $T^{r}_{n}Y$ of $(r,n)$-velocities as well as its open, $L^{r}_{n}$-invariant, dense submanifold $\Imm …

Sharp stability threshold found for deep residual architectures.

Study uses neural networks to predict wall quantities in turbulent flows.

Framework solves physics-constrained inverse problems with limited data.

In this study, the concept of dual Lorentzian homotetic exponential motions in is discussed and their velocities, accelerations obtained. Also, some geometric results between velocity and acceleration vectors of a point in a spatial motion are obtained. Finally, the theorems related to acceleration and acceleration cen…

The paper develops a new class of financial market models. These models are based on generalized telegraph processes: Markov random flows with alternating velocities and jumps occurring when the velocities are switching. While such markets may admit an arbitrage opportunity, the model under consideration is arbitrage-f…

Neural net reconstructs dark matter density from halo velocities.