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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.

168,742 papers · 148 categories

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4 results for “tortuosity”

Predicting Porosity, Permeability, and Tortuosity of Porous Media from Images by Deep Learningphysics.comp-ph

CNNs predict porosity, permeability, and tortuosity from porous media images.

problem Predicting key properties of porous media from images.

method Convolutional neural networks (CNNs) trained with lattice Boltzmann simulations.

result CNNs accurately predict porosity, permeability, and tortuosity.

CNNs porous media porosity permeability tortuosity

2020-07-06abs ↗pdf ↗

Fitting Splines to Axonal Arbors Quantifies Relationship between Branch Order and Geometryq-bio.NC

The study quantifies geometric differences between axonal branches using splines.

problem Neuromorphology's focus on macroscopic features neglects neuron internal geometry.

method Fitting splines to neuron traces, using Frenet-Serret formulas to compute curvature and torsion.

result Parameters of curvature and torsion are distributed differently between axonal branches.

neuromorphology axonal arbors splines Frenet-Serret curvature

2021-04-04abs ↗pdf ↗

A 2D Levy-flight model for the complex dynamics of real-life financial marketsq-fin.ST

Modeling financial market dynamics with 2D Levy flights.

problem Capturing the complex, scaling laws in financial market dynamics.

method 2D Lévy flight model applied to S\&P 500 index prices.

result Empirical spectral properties match model predictions.

financial markets 2D Levy flights scaling laws random matrix theory spectral properties

2022-02-24abs ↗pdf ↗

ML-LBM: Machine Learning Aided Flow Simulation in Porous Mediaphysics.flu-dyn

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

problem Accurate simulation of fluid flow in complex porous media requires excessive computational resources.

method Combining deep learning with direct simulation, using Gated U-Net CNNs trained on datasets of 2D and 3D porous media.

result Deep learning predictions can reach over 90% accuracy for permeability estimation and accelerate simulations by orders of magnitude.

fluid flow porous media machine learning Deep Learning Convolutional Neural Networks

2020-04-22abs ↗pdf ↗