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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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48 results for Fluid Limits

Order positions are key variables in algorithmic trading. This paper studies the limiting behavior of order positions and related queues in a limit order book. In addition to the fluid and diffusion limits for the processes, fluctuations of order positions and related queues around their fluid limits are analyzed. As a…

2015-05-18abs ↗pdf ↗

This paper resolves the stiff fluid case for orthogonal Bianchi B fluids near the singularity.

problem The asymptotic behavior of orthogonal Bianchi class B stiff fluids near the initial singularity.
method Expansion-normalised variables and convergence analysis of the Jacobs set.
result All solutions converge to a specific subset of the Jacobs set, differing from non-stiff cases.

Smartfluidnet accelerates Eulerian fluid simulation with neural networks.

problem Current neural network methods for Eulerian fluid simulation lack flexibility and generalization.
method Smartfluidnet automates model generation and dynamic switching to meet user requirements.
result Smartfluidnet achieves 1.46x and 590x speedup compared to state-of-the-art models, with better simulation quality.

In this paper, we establish a fluid limit for a two--sided Markov order book model. Our main result states that in a certain asymptotic regime, a pair of measure-valued processes representing the "sell-side shape" and "buy-side shape" of an order book converges to a pair of deterministic measure-valued processes in a c…

2014-11-27abs ↗pdf ↗

A machine learning model captures non-Newtonian fluid dynamics from molecular details.

problem Creating accurate non-Newtonian fluid models from molecular data.
method Developed a machine learning framework that maps micro-scale polymer configurations to macro-scale fluid dynamics, preserving molecular fidelity.
result The deep non-Newtonian model (DeePN2^2) accurately predicts fluid behavior without empirical closures.

New method for fluid approximation of CTMCs without population structure.

problem Approximating the macro-scale behavior of large CTMCs.
method Spectral analysis of CTMC transition matrix, diffusion maps, Gaussian process regression.
result Construct an ODE approximating CTMC mean in continuous space.

Proves existence and uniqueness of rotating fluid bodies in GR to second order.

problem Understanding rotating fluid bodies in GR, especially beyond Newtonian limits.
method Second order perturbation theory, derived from first principles, with rigidly rotating finite perfect fluid ball assumptions.
result Equatorially symmetric spacetime determined by central pressure and uniform angular velocity.

Model simulates correlation emergence in two coupled limit order books.

problem Modeling correlation emergence in coupled limit order books.
method Simulated two coupled diffusive limit order books using random walks in the fluid limit, with trader interactions.
result Demonstrated the recovery of an Epps effect from the model.

Novel deep learning approach for fast, differentiable fluid simulations.

problem Challenges in solving incompressible fluid dynamics equations efficiently.
method Physics-constrained training approach for convolutional neural networks.
result Trained models can handle various fluid phenomena and offer fast simulations.

FLUID-LLM uses LLMs to predict fluid dynamics with improved accuracy.

problem Leveraging LLMs for CFD due to their pattern recognition abilities but struggles with fluid dynamics complexities.
method Combines pre-trained LLMs with spatiotemporal-aware encoding to predict unsteady fluid dynamics.
result Significant performance improvements in CFD predictions across various datasets.

The study examines perfect fluid spacetimes and their properties.

problem Characterizing properties of perfect fluid spacetimes with concircular vector fields.
method Analyzing the conformal curvature tensor, state equation, and solitons in perfect fluid spacetimes.
result Perfect fluid spacetimes with concircular vector fields have specific properties related to the state equation and solitons.

The paper finds conditions for pseudosymmetric spacetimes to be perfect fluids.

problem Characterizing pseudosymmetric spacetimes as perfect fluids.
method Analyzes generalized Robertson-Walker spacetimes, conformally flat spacetimes, and dust fluids.
result Conditions for pseudosymmetric spacetimes to be perfect fluids are established.

Study fluid spacetimes, proving shear-free implies vanishing expansion or vorticity.

problem Understanding shear and vorticity in perfect-fluid spacetimes.
method Analyzing perfect-fluid spacetimes using Weyl tensor and divergence.
result Proves shear-free implies vanishing expansion or vorticity for perfect fluids.

Generative model creates fluid simulations from parameters.

problem Creating fast and accurate fluid simulations from parameters.
method Convolutional neural network trained on parameterized fluid data with a novel loss function.
result Generative model accurately approximates fluid simulations and handles complex parameterizations.

This research smooths out fluid equations to avoid sudden shocks.

problem Formation of shock singularities in compressible fluid equations.
method Information geometric regularization of unidimensional pressureless Euler equations.
result Smooth global solutions without artificial viscosity.

The paper studies Ricci solitons in perfect fluid spacetimes with specific vector fields.

problem Analyzing Ricci solitons in perfect fluid spacetimes with torse-forming vector fields.
method Examined perfect fluid spacetimes with torse-forming vector fields ξ, determined Ricci solitons, and classified their behavior as expanding, steady, or shrinking.
result Conditions for the behavior of Ricci solitons in these spacetimes were identified.

Simulates financial market orders using anomalous diffusion models.

problem Anomalous diffusion in financial market order dynamics.
method Discrete Time Random Walk with Sibuya waiting times, non-uniform sampling, and cubic spline interpolation.
result Demonstrates price impact for different forcing functions and model parameters.

New conditions for GRW space-times to be perfect-fluid space-times.

problem Conditions for GRW space-times to be perfect-fluid.
method Gray's decomposition of the gradient of the Ricci tensor, determining Ricci tensor forms in invariant subspaces.
result For most GRW space-times, the Ricci tensor is Einstein or perfect fluid.

Study on static perfect fluid space-time geometry and boundary estimates.

problem Investigate the geometry and boundary properties of static perfect fluid space-time.
method Used generalized Reilly's formula to establish geometric inequalities and boundary estimates.
result Obtained new boundary estimates involving the Brown-York mass and first eigenvalue of the Jacobi operator.

Certain solutions of a sextic sigma-model Lagrangian reminiscent of Skyrme model correspond to perfect fluids with stiff matter equation of state. We analyse from a differential geometric perspective this correspondence extended to general barotropic fluids.

2010-06-06abs ↗pdf ↗

Using a simple and well-motivated modification of the stress-energy tensor for a viscous fluid proposed by Lichnerowicz, we prove that Einstein's equations coupled to a relativistic version of the Navier-Stokes equations are well-posed in a suitable Gevrey class if the fluid is incompressible and irrotational. These la…

2013-10-07abs ↗pdf ↗

RotEqNet preserves rotation symmetry in fluid systems using high-order tensors.

problem Lack of rotational symmetry in machine learning models for fluid systems.
method Introduces RotEqNet, a network that guarantees rotation-equivariance for high-order tensors.
result RotEqNet reduces errors and maintains rotation-equivariance in fluid systems.

This research optimizes fluid-dynamic designs using deep learning and active learning.

problem Expensive and limited empirical design verification for fluid dynamics.
method Applied a deep learning architecture to predict and optimize fluid dynamics performance.
result Reduced the number of required simulation data points from ~8000 to 625.

Generative models improve carbon storage site prediction using Bayesian inversion.

problem Predicting suitable geologic sites for long-term carbon dioxide storage.
method Generative adversarial networks and Bayesian inversion to condition models on physical measurements and historic data.
result Improved resolution of carbon dioxide storage capacity forecasts.

Study of kk-almost Yamabe solitons in perfect fluid spacetimes.

problem Analyzing kk-almost Yamabe solitons in perfect fluid spacetimes.
method Examined perfect fluid spacetimes and kk-almost Yamabe solitons using Einstein field equations.
result Characterized properties of kk-almost Yamabe solitons in perfect fluid spacetimes.