arXiv research

Log-ergodic model improves velocity of money prediction.

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

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

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

New method distinguishes cause from effect using causal velocity.

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

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

NN-Turb generates turbulent velocity statistics using neural networks.

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

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

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…

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…

DSNE visualizes data velocity in lower dimensions.

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…

TVM improves generative modeling by matching terminal velocities.

Neural solver computes Wasserstein geodesics and velocity fields efficiently.

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

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…

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

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

Derives RL framework for systems without velocity or acceleration measurements.

In this paper the dependence of wealth distribution and the velocity of money on the required reserve ratio is examined based on a random transfer model of money and computer simulations. A fractional reserve banking system is introduced to the model where money creation can be achieved by bank loans and the monetary a…

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…

Neural networks predict flow and elastic stresses in viscoelastic turbulence.

Generative models on spheres improve discrete sequence sampling.

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

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

Study develops efficient algorithm for probabilistic penetration response of composite plates.

Flow Matching models help generative models stay within the subspace of real data.

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

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…

Generative sampler learns velocity fields for efficient posterior inference.

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 …

We study velocity correlations induced by diffusion and dissipation in a simple dissipative dynamical system. We observe that diffusion, as a result of time reversible microscopic processes, leads to correlations with different spatial parity from those caused by dissipation, consisting of time irreversible microscopic…

Neural network predicts turbulence near-wall regions efficiently.

DFM models are analyzed for generating distributions with provable convergence.

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…

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of…

The paper explores rectified flows and their relation to optimal transport.

Neural net reconstructs dark matter density from halo velocities.

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…

We have studied the statistical mechanics of money circulation in a closed economic system. An explicit statistical formulation of the circulation velocity of money is presented for the first time by introducing the concept of holding time of money. The result indicates that the velocity is governed by behavior pattern…

In this paper, we propose a nonlinear distance metric learning scheme based on the fusion of component linear metrics. Instead of merging displacements at each data point, our model calculates the velocities induced by the component transformations, via a geodesic interpolation on a Lie transfor- mation group. Such vel…

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

This paper investigates the position (state) distribution of the single step binomial (multi-nomial) process on a discrete state / time grid under the assumption that the velocity process rather than the state process is Markovian. In this model the particle follows a simple multi-step process in velocity space which a…

Clarifies method of phase synchronization for decoupling linear differential equations.

A time schedule simplifies learning in flow-based models for high-dimensional data.