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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.

169,051 papers · 148 categories

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371013 · Apr 201919922001200920182026
48 results for fluid bolus therapy

Study predicts blood pressure response to fluid bolus therapy with high accuracy.

problem Predicting successful response to fluid bolus therapy in hypotensive ICU patients.
method Used attention-based LSTM and GRU neural networks on a large ICU database.
result Stacked LSTM with attention mechanism achieved highest accuracy of 0.852.

SODA-RL learns diverse treatment options for hypotension from data.

problem Identifying the best treatment for acute hypotension from observational data.
method SODA-RL: Safely Optimized, Diverse, and Accurate Reinforcement Learning.
result SODA-RL identifies distinct, plausible treatment options from observational data.

Reinforcement Learning improves insulin bolus decisions for type-I diabetes patients.

problem Optimal insulin bolus decisions for type-I diabetes patients are not well-established.
method Applied Reinforcement Learning to simulated T1DM data.
result Optimal bolus rule differs from standard advisors and can prevent hypoglycemia.

This study improves early prediction of CPAP therapy compliance.

problem Predicting early compliance with CPAP therapy to improve patient outcomes.
method Built classifiers for compliance at three CPAP therapy follow-up points.
result Month 3 classifier achieved highest f1-score of 87% in cross-validation and test.

We consider the kinematics of specific fluid spacetimes admitting timelike congruences of Ricci Solitons. These fluids includes string cloud, string fluid, perfect fluid, radially symmetric fluid, anisotropic fluid and relativistic magneto-fluid. Results are obtained and important physical aspects are discussed.

2017-09-07abs ↗pdf ↗

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.

Generative adversarial networks model and generate physical therapy exercises.

problem Mathematical modeling of human movements in physical therapy.
method Generative adversarial network structure with discriminative and generative models trained concurrently.
result Ability to classify and generate motion examples that resemble recorded sequences.

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.

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…

2018-06-06abs ↗pdf ↗

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.

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.

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.

Proposes a framework for automated radiation therapy treatment planning with uncertainty quantification.

problem Quantifying uncertainties in dose-related quantities for automated treatment planning.
method Three-step pipeline: feature extraction, dose statistic prediction, and dose mimicking.
result Probabilistic treatment plans agree better with clinical counterparts than non-probabilistic ones.

The paper explores the geometric properties of fluid flows and their symmetries.

problem Understanding the geometric properties of fluid flows and their symmetries.
method Analyzing the Euler equation and its relation to geodesic flows on groupoids of multiphase diffeomorphisms.
result Generalized flows, multiphase fluids, and vortex sheets are all geodesics on certain groupoids of multiphase diffeomorphisms.

The paper studies geometric structures in perfect fluid spacetimes with specific metrics.

problem Analyzing the geometric properties of perfect fluid spacetimes with specific metrics.
method Investigates conditions for conformal Ricci-Yamabe soliton and derives Laplace equations.
result Conditions for expanding, steady, or shrinking conformal Ricci-Yamabe solitons are identified.

There is a remarkable and canonical problem in 3D geometry and topology: To understand existing models of 3D fluid motion or to create new ones that may be useful. We discuss from an algebraic viewpoint the PDE called Euler's equation for incompressible frictionless fluid motion. In part I we define a "finite dimension…

2010-10-13abs ↗pdf ↗

Paper extends port-Hamiltonian model to include internal energy for compressible and incompressible flow.

problem Modeling fluid flow dynamics with internal energy and constraints.
method Derived port-Hamiltonian model using interconnection maps and added internal energy and constraint forces.
result Model accurately represents both compressible and incompressible fluid flow.

This paper is a rigorous study of two dual pairs of momentum maps arising in the context of fluid equations whose configuration Lie group is the group of automorphism of a trivial principal bundle, generically called here non-abelian fluids. It is shown that the actions involved are mutually completely orthogonal, whic…

2013-04-18abs ↗pdf ↗

SPH-ParVI uses fluid dynamics to sample unknown densities efficiently.

problem Sampling partially known densities or using gradients in probabilistic models.
method Smoothed Particle Hydrodynamics (SPH) for modeling fluid dynamics to approximate target densities.
result SPH-ParVI provides fast, flexible, scalable, and deterministic sampling for Bayesian inference and generative models.

Study shows instability of naked singularities in perfect fluid models.

problem Instability of naked singularities in Einstein equations coupled with isothermal perfect fluid.
method Investigated spherically symmetric self-similar naked singularities under C1,αC^{1,α} perturbations of an external massless scalar field.
result Spherically symmetric self-similar naked singularities are unstable to trapped surface formation.