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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.

168,786 papers · 148 categories

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48 results for wind tunnel testing

Machine learning reduces wind tunnel testing costs for tall buildings.

problem Limited wind tunnel tests fail to fully reveal interference effects of tall buildings.
method Used machine learning techniques, including GANs, to predict pressure coefficients.
result GANs model based on 30% of dataset accurately predicts pressure coefficients under unseen conditions.

Bayesian and POD methods fuse noisy wind tunnel and simulated aerodynamic data.

problem Fusing data from wind tunnel measurements and numerical simulations for accurate aerodynamic modeling.
method Bayesian and Proper Orthogonal Decomposition (POD) methods to infer true aerodynamic fields.
result Bayesian method is more robust with scarce data and accounts for uncertainties.

Methodology extrapolates wind fields from sparse data with uncertainty quantification.

problem Extrapolating wind fields from limited measurements with uncertainty.
method Nonparametric Bayesian dictionary learning for sparse/incomplete data.
result Enhanced extrapolation accuracy, even in high-dimensional data.

Transformers mimic Bayesian reasoning in controlled settings, revealing geometric mechanisms.

problem Verifying if transformers perform Bayesian reasoning rigorously in natural data.
method Constructing Bayesian wind tunnels with known posteriors and proving memorization impossibility.
result Transformers achieve 10310^{-3}-10410^{-4} bit accuracy in Bayesian posteriors, while MLPs fail.

DeepMIDE forecasts wind speeds across space, time, and height for offshore wind energy.

problem Forecasting wind speeds across multiple heights for large offshore wind turbines.
method Statistical deep learning model that jointly models wind speeds at different heights using a multi-output integro-difference equation.
result DeepMIDE forecasts outperform traditional methods in real-world offshore wind energy data.

This is the first of three papers that refine and extend portions of our earlier preprint, "Depth of a knot tunnel." Together, they rework the entire preprint. H. Goda, M. Scharlemann, and A. Thompson described a general construction of all tunnels of all tunnel number 1 knots using "tunnel moves". We apply the theory …

2008-12-07abs ↗pdf ↗

It is unknown whether an unknotting tunnel is always isotopic to a geodesic in a finite volume hyperbolic 3-manifold. In this paper, we address the generalization of this problem to hyperbolic 3-manifolds admitting tunnel systems. We show that there exist finite volume hyperbolic 3-manifolds with a single cusp, with a …

2013-02-22abs ↗pdf ↗

This is the second of three papers that refine and extend portions of our earlier preprint, "The depth of a knot tunnel." Together, they rework the entire preprint. The theory of tunnel number 1 knots that we introduced in "The tree of knot tunnels" yields a parameterization in which each tunnel is described uniquely b…

2008-12-07abs ↗pdf ↗

We present a new theory which describes the collection of all tunnels of tunnel number 1 knots in the 3-sphere (up to orientation-preserving equivalence in the sense of Heegaard splittings) using the disk complex of the genus-2 handlebody and associated structures. It shows that each knot tunnel is obtained from the tu…

2006-11-29abs ↗pdf ↗

For a genus-1 1-bridge knot in the 3-sphere, that is, a (1,1)-knot, a middle tunnel is a tunnel that is not an upper or lower tunnel for some (1,1)-position. Most torus knots have a middle tunnel, and non-torus-knot examples were obtained by Goda, Hayashi, and Ishihara. We generalize their construction and calculate th…

2011-08-17abs ↗pdf ↗

The theory of tunnel number 1 knots detailed in our previous paper, The tree of knot tunnels, provides a non-negative integer invariant called the depth of the tunnel. We give various results related to the depth invariant. Noting that it equals the minimum number of Goda-Scharlemann-Thompson tunnel moves needed to con…

2007-08-24abs ↗pdf ↗

It is proven here that if the connected sum of two tunnel number one knots in the 3-sphere is a tunnel number two knot, then at least one of the summand knots has a genus two Heegaard splitting with a meridian as a primitive element. Hence this is a necessary and sufficient condition for tunnel number one knots to have…

1999-06-10abs ↗pdf ↗

For a genus-1 1-bridge knot in the 3-sphere, that is, a (1,1)-knot, a middle tunnel is a tunnel that is not an upper or lower tunnel for some (1,1)-position. Most torus knots have a middle tunnel, and non-torus-knot examples were obtained by Goda, Hayashi, and Ishihara. In a previous paper, we generalized their constru…

2011-08-18abs ↗pdf ↗

This is the third of three papers that refine and extend portions of our earlier preprint, "The depth of a knot tunnel." Together, they rework the entire preprint. In this paper, we use the theory of tunnel number 1 knots that we introduced in "The tree of knot tunnels" to strengthen the Tunnel Leveling Theorem of H. G…

2008-12-07abs ↗pdf ↗

In "Tunnel one, fibered links", the second author showed that the tunnel of a tunnel number one, fibered link can be isotoped to lie as a properly embedded arc in the fiber surface of the link. In this paper, we analyze how the arc behaves under the monodromy action, and show that the tunnel arc is nearly clean, with t…

2013-12-25abs ↗pdf ↗

The paper studies tunnel and bridge numbers of composite genus 2 spatial graphs.

problem Understanding the tunnel and bridge numbers of composite genus 2 spatial graphs.
method Analyzes connected sum and trivalent vertex sum operations on genus 2 spatial graphs, proving bounds for tunnel and bridge numbers.
result Sharp bounds for the tunnel number of composite genus 2 spatial graphs, including lower bounds for bridge numbers.

Transfer Learning (TL) in Deep Neural Networks is gaining importance because in most of the applications, the labeling of data is costly and time-consuming. Additionally, TL also provides an effective weight initialization strategy for Deep Neural Networks . This paper introduces the idea of Adaptive Transfer Learning …

2018-10-30abs ↗pdf ↗

We show that the set of cusp shapes of hyperbolic tunnel number one manifolds is dense in the Teichmuller space of the torus. A similar result holds for tunnel number n manifolds. As a consequence, for fixed n, there are infinitely many hyperbolic tunnel number n manifolds with at most one exceptional Dehn filling. Thi…

2017-11-10abs ↗pdf ↗

This paper combines and improves probabilistic forecasts of wind speeds using advanced statistical methods.

problem Improving accuracy and reliability of probabilistic forecasts in wind speed prediction.
method Adapting prediction with expert advice theory to probabilistic forecasts, combining raw or post-processed ensembles, and using CRPS and Jolliffe-Primo tests.
result Combining probabilistic forecasts can lead to more reliable and skillful predictions, as shown by the Jolliffe-Primo test.

It is a consequence of theorems of Gordon-Reid [Tangle decompositions of tunnel number one knots and links, J. Knot Theory and its Ramifications, 4 (1995) 389-409] and Thompson [Thin position and bridge number for knots in the 3-sphere, Topology, 36 (1997) 505-507] that a tunnel number one knot, if put in thin position…

1999-10-19abs ↗pdf ↗

We show there exist tunnel number one hyperbolic 3-manifolds with arbitrarily long unknotting tunnel. This provides a negative answer to an old question of Colin Adams.

2008-12-04abs ↗pdf ↗

A knot in the 3-sphere in genus-1 1-bridge position (called a (1,1)-position) can be described by an element of the braid group of two points in the torus. Our main results tell how to translate between a braid group element and the sequence of slope invariants of the upper and lower tunnels of the (1,1)-position. Afte…

2010-06-27abs ↗pdf ↗

In a previous paper the authors defined the growth rate of the tunnel number of knots, an invariant that measures that asymptotic behavior of the tunnel number under connected sum. In this paper we calculate the growth rate of the tunnel number of m-small knots in terms of their bridge indices.

2015-06-12abs ↗pdf ↗

Let K be a tunnel number one, fibered link in S^3, with fiber F, and unknotting tunnel ττ. We show that ττ can be isotoped to lie in F.

2010-12-15abs ↗pdf ↗

Study uses CNNs to upscale wind speed data from 100 km to 3 km, improving subgrid-scale variability.

problem Recovering fine-scale wind speed information from coarse data.
method Convolutional neural networks (CNNs) with different input configurations (coarse wind speed, fine-scale topography, diurnal cycle) were tested.
result CNN models with coarse wind and fine topography inputs perform best in generalizing to unseen regions.

Applications of Quantum Tunneling effect have long gone beyond the traditional physical meaning. Initially created by Gamow to explain α-decay of nuclear particles, along the time, quantum tunneling found fertile domain of research in chemistry and recently in biology, where the new discipline of Quantum Biology emerge…

2013-07-25abs ↗pdf ↗

Hybrid model improves wind speed prediction accuracy using MLP and WOA.

problem Improving wind speed prediction accuracy for renewable energy control.
method Combining MLP with Whale Optimization Algorithm (WOA) for data preprocessing and model optimization.
result The hybrid MLP-WOA model outperformed standalone MLP model in wind speed prediction accuracy.

It is shown phenomenologically that the fractional derivative ξ=Dαuξ=D^αu of order αα of a multifractal function has a power-law tail ξp\propto |ξ| ^{-p_\star} in its cumulative probability, for a suitable range of αα's. The exponent is determined by the condition ζp=αpζ_{p_\star} = αp_\star, where ζpζ_p is the exponent of…

2001-07-25abs ↗pdf ↗

We determine the genus one fibered knots in lens spaces that have tunnel number one. We also show that every tunnel number one, once-punctured torus bundle is the result of Dehn filling a component of the Whitehead link in the 3-sphere.

2006-06-15abs ↗pdf ↗

In this paper, we show that any unknotting tunnel for a two bridge knot is isotopic to either one of known ones. This together with Morimoto-Sakuma's result gives the complete classification of unknotting tunnels for two bridge knots up to isotopies and homeomorphisms.

1999-11-20abs ↗pdf ↗

Novel approach embeds loss tunnels in neural networks, revealing insights into their structure.

problem Understanding the structure of neural network loss surfaces, especially low-loss tunnels.
method Directly embedding loss tunnels into the loss landscape of neural networks.
result Improved insights into the length and structure of loss tunnels, and better subspace inference in Bayesian neural networks.

Experimentally identified 22 L-space knots with tunnel number >1, some having high genus and braid index.

problem Identifying L-space knots with tunnel number greater than 1.
method Cataloging hyperbolic manifolds, using SnapPy and KLO to find knot presentations as closures of positive braids.
result Found 9 asymmetric L-space knot complements with tunnel number 2, and 22 with tunnel number 1.

Paper finds first infinite family of hyperbolic knots with specific properties.

problem Identifying new hyperbolic knots with specific properties.
method Examined SnapPy census and used knot theory to find new infinite family.
result First infinite family of strongly invertible hyperbolic L-space knots with braid index four and tunnel number two.

In a previous paper Kobayashi and Rieck defined the growth rate of the tunnel number of a knot KK, a knot invariant that measures the asymptotic behavior of the tunnel number under iterated connected sum of KK. We denote the growth rate by $\mbox{gr}_t(K)$. In this paper we construct, for any ε>0ε> 0, a hyperbolic kno…

2015-07-13abs ↗pdf ↗