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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.

168,694 papers · 148 categories

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48 results for pressure term

Study how large-scale flows align small-scale vortices in 3D Euler equations.

problem Understanding how large-scale flows align small-scale vortices in 3D Euler equations.
method Constructing a Lagrangian coordinate to identify when the Lie bracket is zero and investigating the locality of the pressure term.
result Clarified conditions under which small-scale vortices are aligned by large-scale flows.

The paper examines conditions for conformal Ricci solitons on generalized (κ,μκ, μ)-space forms.

problem Conditions for conformal Ricci solitons on generalized (κ,μκ, μ)-space forms.
method Derivation of conditions for solitons to be shrinking, steady, or expanding in terms of conformal pressure p.
result Conditions for a Ricci semi-symmetric generalized (κ,μκ, μ)-space form to form an Einstein manifold when equipped with a conformal Ricci soliton.

This paper describes the Pressure Ulcers Online Website, which is a first step solution towards a new and innovative platform for helping people to detect, understand and manage pressure ulcers. It outlines the reasons why the project has been developed and provides a central point of contact for pressure ulcer analysi…

2018-08-17abs ↗pdf ↗

The study examines entropy and pressure at infinity in negatively curved manifolds, linking them to strong positive recurrence.

problem Investigating strong positive recurrence in negatively curved manifolds.
method Defining and comparing entropy and pressure at infinity through different measures.
result Strong positive recurrence potentials admit finite Gibbs measures.

Deep learning speeds up pressure prediction in carbon storage reservoirs.

problem Accurately forecasting reservoir pressure in geologic carbon storage projects with sparse well data.
method Combining InSAR surface displacement data with deep learning and data assimilation techniques.
result Workflow can predict reservoir pressure with high efficiency and uncertainty quantification.

The paper establishes pressure gaps for manifolds with flat subtori singularities.

problem Understanding phase transitions in nonpositively curved manifolds with flat subtori.
method Derives a pressure gap criterion for closed rank 1 manifolds with specific singular sets and proves Hölder continuity of geometric potentials.
result Geometric potentials have pressure gaps and no phase transitions under certain curvature constraints.

Extends Penrose's method to null shells with pressure and energy flux.

problem Constructing null thin shells with arbitrary gravitational/matter content.
method Derive locally Lipschitz metric and coordinate transformation.
result Example of null shell with non-trivial energy density, flux, and pressure in Minkowski space.

Study develops a data-based model for in-cylinder pressure and cyclic variations in RCCI engines.

problem Lack of models capturing cyclic variations in combustion concepts like RCCI.
method Combines Principle Component Decomposition and Gaussian Process Regression.
result Model predicts combustion measures with high accuracy, especially peak-pressure rise-rate.

Study forecasts aortic pressure with deep learning models.

problem Forecasting noisy, non-stationary aortic pressure.
method Used deep learning models, specifically recurrent neural networks with Legendre Memory Unit, on 25 Hz time series data.
result Recurrent neural networks with Legendre Memory Unit achieved the best performance with an overall forecasting error of 1.8 mmHg.

The paper studies Blaschke products, proving uniformization and non-degeneracy of pressure metrics.

problem Analytic aspects of Blaschke products and their moduli space.
method Definition of complex structure and proof of uniformization theorem.
result Pressure semi-norms are non-degenerate outside the super-attracting locus.

Model predicts trading strategies based on latent demand and price impact.

problem Predicting strategic trading behavior of investors with private targets.
method Equilibrium model of dynamic trading, learning, and pricing by strategic investors.
result Trading strategies are a combination of target following, liquidity provision, and front-running based on latent demand and price pressure.

The paper defines a path metric on a stable component of polynomial families.

problem Understanding the geometry of polynomial families with parabolic relations.
method Constructing a positive semi-definite pressure form on a bounded stable component of the moduli space.
result The pressure form defines a path metric on the stable component.

In this paper, we analyzed the physical meaning of scalar curvatures for a generalized Riemannian space. It is developed the Madsen's formulae for pressures and energy-densities with respect to the corresponding energy-momentum tensors. After that, the energy-momentum tensors, pressures, energy-densities and state-para…

2019-11-13abs ↗pdf ↗

We prove that the Hitchin parametrization provides geodesic coordinates at the Fuchsian locus for the pressure metric in the Hitchin component H3(S)\mathcal{H}_{3}(S) of surface group representations into PSL(3,R)PSL(3,\mathbb{R}). The proof consists of the following elements: we compute first derivatives of the pressure metric…

2019-10-02abs ↗pdf ↗
Ponzi Fundsq-fin.GN

Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.

problem Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.
method Decomposed fund returns into price pressure and fundamental components, and identified the impact of price chasing on fund flows.
result Funds' self-inflated returns lead to wealth reallocation and market crashes, and can be predicted by fund illiquidity.

This paper defines the pressure metric on the Moduli space of Margulis spacetimes without cusps and shows that it is positive definite on the constant entropy sections. It also demonstrates an identity regarding the variation of the cross-ratios.

2015-05-04abs ↗pdf ↗

Study on MHD equilibria on curved spaces without symmetries.

problem Analyzing MHD equilibria on curved spaces without symmetries.
method Examined MHD equilibria on Riemannian 3-manifolds with various adapted metrics.
result Found that for an open and dense set of adapted metrics, MHD equilibria on compact 3-manifolds without boundary admit no continuous Killing symmetries.

Bayesian inference calibrates Hall thruster model uncertainty at varying pressures.

problem Quantifying uncertainty in a multi-component Hall thruster model at different facility pressures.
method Bayesian inference applied to calibrate and quantify prediction uncertainty in a coupled multi-component Hall thruster model.
result Model reduces predictive errors in thrust and discharge current by more than 50% compared to a previous model.

Hybrid model predicts flow and pressure in water systems.

problem Predicting flow and pressure in water distribution systems with complex spatial-temporal correlations.
method Hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN).
result Our model outperformed 9 baseline models in flow and pressure series prediction.

Scaling relations, such as the IPAT equation and the Kaya identity, are useful for quickly gauging the scale of economic, technological, and demographic changes required to reduce environmental impacts and pressures; in the case of the Kaya identity, the environmental pressure is greenhouse gas emissions. However, when…

2011-12-04abs ↗pdf ↗

A new method splits surface flow discretizations into streamfunctions and harmonic fields.

problem Discretizing incompressible flows on surfaces with pressure and saddle-point structure.
method Discrete Helmholtz-Hodge decomposition for BDM elements on surfaces.
result Eliminates pressure and saddle-point structure, ensuring exact tangentiality and divergence-freeness.

Study equilibrium measures on manifolds without conjugate points with visibility covering.

problem Uniqueness and properties of equilibrium measures on manifolds without conjugate points.
method Analysis of geodesic flows, study of equilibrium measures, ergodic properties, and pressure gap.
result Equilibrium measures satisfy a weak pressure gap under certain conditions.

Deep learning has been used in many areas, such as feature detections in images and the game of go. This paper presents a study that attempts to use the deep learning method to predict turbomachinery performance. Three different deep neural networks are built and trained to predict the pressure distributions of turbine…

2018-06-12abs ↗pdf ↗

The main result is an explicit expression for the Pressure Metric on the Hitchin component of surface group representations into PSL(n,R) along the Fuchsian locus. The expression is in terms of a parametrization of the tangent space by holomorphic differentials, and it gives a precise relationship with the Petersson pa…

2015-06-04abs ↗pdf ↗

The dynamics of representations into PSL_d(R) are studied for surfaces of genus at least 3.

problem Dynamics of representations into PSL_d(R) for surfaces of genus at least 3.
method Showed quasi-convex subsets of infinite diameter for the Weil--Petersson metric have finite diameter for the path metric of the pressure metric through controlled bounded length of biinfinite paths of bending deformations.
result Biinfinite paths of bending deformations have controlled bounded length.

The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty. Thirty-six adults walked on a t…

2019-07-24abs ↗pdf ↗

Study evaluates uncertainty in BP estimation from PPG signals under domain shift.

problem Uncertainty quantification in healthcare, especially for cuffless BP estimation.
method Compared deep ensembles, Monte Carlo dropout, and various recalibration techniques.
result Deep ensembles provide stronger robustness under domain shift.

An innovative method optimizes engine calibration to improve efficiency and reduce emissions.

problem Complex engines with many tunable parameters require efficient calibration methods.
method Combines Principal Component Decomposition with constrained Bayesian Optimization to minimize pressure curve deviation.
result Optimal engine calibration found after 64.4s with a 0.017% efficiency gain.

We consider insurance derivatives depending on an external physical risk process, for example a temperature in a low dimensional climate model. We assume that this process is correlated with a tradable financial asset. We derive optimal strategies for exponential utility from terminal wealth, determine the indifference…

2007-05-25abs ↗pdf ↗

Study infinitesimal characters on semi-groups to prove interior properties and apply to Teichmüller spaces.

problem Properties of infinitesimal characters and their applications in Teichmüller spaces.
method Analyzing integrable tangent vectors on character varieties and applying to pressure forms and Teichmüller spaces.
result Non-empty interior of the cone of Jordan variations and length-normalized variations for split groups.