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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.

168,695 papers · 148 categories

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48 results for Helmholtz principle

An algorithm detects anomalies based on human perception principles.

problem Anomaly detection in data.
method Inspired by Gestalt psychology and Helmholtz principle, the algorithm models anomalies as unexpected elements in random distributions.
result The algorithm efficiently detects anomalies with minimal user intervention and promising results on multivariate data.

The goal of this paper is to describe and clarify as much as possible the 3-dimensional topology underlying the Helmholtz cuts method, which occurs in a wide theoretic and applied literature about Electromagnetism, Fluid dynamics and Elasticity on domains of the ordinary space. We consider two classes of bounded domain…

2010-01-25abs ↗pdf ↗

Modified Gibbs-Helmholtz equation geometric models for thermodynamics.

problem Geometric interpretation of Gibbs-Helmholtz equation in thermodynamics.
method Developed new holonomic and non-holonomic geometric models associated to Gibbs-Helmholtz equation.
result Characterized equivalence between Gibbs-Helmholtz entropy and other entropies.

New findings on Helmholtz equation solutions show exponential growth in constant for three ball inequality.

problem Analyzing solutions of Helmholtz equation on different manifolds.
method Examining the three ball inequality for solutions of Helmholtz equation on Rn\mathbb{R}^n, Sn\mathbb{S}^n, or Hn\mathbb{H}^n.
result The constant in the three ball inequality grows exponentially with the wave number.

In this paper, we present the point symmetry group of three-dimensional homogeneous Helmholtz equation, when we consider the cylindrical coordinate system. In continuation, we present a complete set of functionally independent invariants of the equation along with the form of the general solution provided by these inva…

2009-08-25abs ↗pdf ↗

Decomposes financial networks to reveal cause-effect hierarchies during crises.

problem Complex financial networks are hard to interpret due to Granger causality.
method Helmholtz-Hodge-Kodaira decomposition to separate networks into rotational and gradient components.
result Precious metals and pharmaceutical products are identified as causal drivers during crises.

Extends GP regression to complex Helmholtz problems, improving wavefield inference in brain elastography.

problem Infer complex Helmholtz wavefields from sparse, noisy data.
method Operator-informed Gaussian processes, realifying complex operator into real blocks, using PDE residuals and boundary traces.
result Competitive with finite-difference and neural-network methods, reconstructs brain shear curl field with high correlation.

We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibility and accuracy in the inferential process. In common with the original Helmholtz machine and later variational autoencoder algorithms (but …

2018-05-28abs ↗pdf ↗

Neural operators improve solving Helmholtz equation for various wave speeds.

problem Neural operators struggle with out-of-distribution scenarios for high-frequency waves.
method Proposed a subfamily of neural operators with stochastic depth for enhanced approximation of the Helmholtz equation.
result Neural operators with stochastic depth outperform standard models in out-of-distribution scenarios.

Rigidity theorem for spherical sectors in Riemannian manifolds.

problem Rigidity of spherical sectors in Riemannian manifolds under overdetermined conditions.
method Analyzing solutions to the inhomogeneous Helmholtz equation with constant Dirichlet and Neumann boundary conditions.
result Spherical sectors are the only solutions under given conditions.

NOHD optimizes multi-agent systems by decomposing dynamics into irrotational and solenoidal components.

problem Non-stationarity and conflicting interests in multi-agent learning problems.
method NOHD (Newton Optimization on Helmholtz Decomposition) decomposes system dynamics into irrotational and solenoidal components.
result NOHD ensures quadratic convergence in purely irrotational and solenoidal systems and attracts to stable fixed points in general multi-agent systems.

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.

Recent research connects Hörmander's old work to modern boundary Laplacian analysis.

problem How close is the Dirichlet-to-Neumann map to the boundary Laplacian?
method Investigates techniques from Hörmander's 1950s manuscript to solve modern boundary Laplacian problems.
result Obtained results for DtN maps on non-smooth boundaries, Helmholtz equation, and differential forms.

We perform global and local analysis of oscillatory and damped spherically symmetric fundamental solutions for Helmholtz operators (Δ±β2)\big({-}Δ\pmβ^2\big) in dd-dimensional, RR-radius hyperbolic HRd{\mathbf H}_R^d and hyperspherical SRd{\mathbf S}_R^d geometry, which represent Riemannian manifolds with positive constant…

2018-03-19abs ↗pdf ↗

Paper explores Fisher-Rao gradient flows and their kernel approximations.

problem Understanding and analyzing approximations of Fisher-Rao gradient flows.
method Rigorous investigation of Fisher-Rao and Wasserstein type gradient flows, focusing on kernel approximations.
result Proves evolutionary Γ-convergence for kernel-approximated Fisher-Rao flows, providing theoretical guarantees.

Defines a filtration on variational bicomplex for concise functional form conditions.

problem Expressing functional form vanishing conditions concisely.
method Introduces a filtration on the variational bicomplex and studies its properties.
result Graded components of the filtration inherit module structures, simplifying functional form conditions.

Efficient unsupervised training and inference in deep generative models remains a challenging problem. One basic approach, called Helmholtz machine, involves training a top-down directed generative model together with a bottom-up auxiliary model used for approximate inference. Recent results indicate that better genera…

2015-06-12abs ↗pdf ↗

We consider the regularity of an interface between two incompressible and inviscid fluids flows in the presence of surface tension. We obtain local in time estimates on the interface in H32k+1H^{\frac32k +1} and the velocity fields in H32kH^{\frac32k}. These estimates are obtained using geometric considerations which show th…

2006-09-20abs ↗pdf ↗

We discuss the problem of the existence of a regular invariant Lagrangian for a given system of invariant second-order differential equations on a Lie group GG, using approaches based on the Helmholtz conditions. Although we deal with the problem directly on TGTG, our main result relies on a reduction of the system on…

2008-01-30abs ↗pdf ↗

Study non-local isoperimetric energies on spheres using a Riemannian autocorrelation function.

problem Analyse non-local isoperimetric energies on spheres.
method Introduce Riemannian autocorrelation function and use it to reformulate and compute the energies.
result Show that the non-local isoperimetric energies can be reformulated and computed using the Riemannian autocorrelation function.

Gaussian processes improved for ocean current reconstruction and divergence identification.

problem Reconstructing ocean currents from sparse buoy data.
method Proposed a Helmholtz decomposition-based approach to Gaussian processes for better physical modeling.
result Improved inference on ocean currents and divergence identification with minimal computational cost.

A 3-dimensional vector field BB is said to be Beltrami vector field (force free-magnetic vector field in physics), if B×(×B)=0B\times(\nabla\times B)=0. Motivated by our investigations on projective an polynomial superflows, and as an important side result, in the first paper on this topic we constructed two unique Beltrami…

2017-01-16abs ↗pdf ↗

Unified framework for forward and inverse PDE problems in multiphase media.

problem Non-differentiable inverse problems in discrete-valued material fields.
method GenPANIS: Latent-variable generative framework preserving discrete microstructures.
result Unified bidirectional inference with minimal labeled pairs and physics-aware decoder.

Deep neural networks improve surrogate models for non-smooth quantities in uncertain geometries.

problem Building accurate surrogates for non-smooth quantities in uncertain geometries.
method Deep neural networks for point evaluation of solutions to interface problems with geometric uncertainties.
result Neural networks provide good surrogates without suffering from the curse of dimensionality.

We discuss intrinsic aspects of Krupka's approach to finite-order variational sequences. We give intrinsic isomorphisms of the quotient subsheaves of the short finite-order variational sequence with sheaves of forms on jet spaces of suitable order, obtaining a new finite-order (short exact) variational sequence which i…

2000-01-05abs ↗pdf ↗

New proof of Gaffney's inequality for differential forms on manifolds with boundary.

problem Proving Gaffney's inequality for differential forms on manifolds with boundary.
method Variational approach combined with Bochner's technique.
result New proof of Gaffney's inequality for differential forms.

The paper explores indeterminacy in latent factor projections and its implications for data representation.

problem Indeterminacy in latent factor projections and its implications for data representation.
method Analyzes the fundamental problem of indeterminacy in latent factor projections and discusses its implications for data representation.
result Latent factor determinacy across all facets is achieved when the feature-dimension grows to infinity.