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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.

169,291 papers · 148 categories

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48 results for Dirac-Frenkel principle

A new principle minimizes residual and introduces momentum to improve PDE solution dynamics.

problem Ill-conditioning in Dirac-Frenkel residual minimization leads to non-unique parameter dynamics.
method Introduces a history variable (momentum) to select better-conditioned parameter velocities, preserving residual minimization while promoting smooth parameter evolutions.
result The approach leads to increased robustness in singular and near-singular PDE solution regimes.

Extends variational principle to tensor Banach spaces.

problem High-dimensional partial differential equations and minimization problems.
method Describes tensor product as disjoint connected components, each modeled as a Banach manifold.
result Extension of Dirac-Frenkel variational principle to topological tensor spaces.

Neural Galerkin schemes use active learning to solve high-dimensional equations.

problem Inaccurate function approximations in high dimensions with limited training data.
method Neural Galerkin schemes based on deep learning with active learning for high-dimensional PDEs.
result Active data collection improves the numerical solution of high-dimensional equations.

The main goal of this paper is to study the geometric structures associated with the representation of tensors in subspace based formats. To do this we use a property of the so-called minimal subspaces which allows us to describe the tensor representation by means of a rooted tree. By using the tree structure and the d…

2015-05-12abs ↗pdf ↗

Research examines how Islamic banking principles spread among managers and scholars.

problem Diffusion of Islamic banking principles among managers and scholars.
method Literature review focusing on knowledge diffusion and Islamic banking governance principles.
result Emergence of common Islamic banking governance principles from diverse knowledge streams.

The paper establishes maximum principles and stochastic completeness for pseudo-Hermitian manifolds.

problem Maximum principles and stochastic completeness for pseudo-Hermitian manifolds.
method Established generalized maximum principles and proved stochastic completeness equivalence.
result Stochastic completeness for the heat semigroup is equivalent to generalized maximum principles.

The h-principle helps solve complex geometric problems.

problem Solving complex geometric problems using the h-principle.
method Developed from the Oka-Grauert principle and Gromov's theory, the h-principle is applied to Oka manifolds and maps.
result Recent developments and applications of the h-principle in complex analysis and geometry.

The study establishes uncertainty principles on harmonic manifolds of rank one.

problem Developing uncertainty principles for harmonic manifolds of rank one.
method Derivation of various uncertainty principles including Heisenberg, Morgen, Schrödinger, and Hömanders principles.
result Generalization of Hausdorff-Young inequality to harmonic manifolds of rank one.

A new method to break down insurance costs into risk and uncertainty.

problem Understanding and quantifying insurance costs in uncertain environments.
method An axiomatic approach to decompose premium principles into risk and deviation measures.
result Maximal risk and minimal deviation measures can be uniquely identified in decompositions.

Proof of Schwarz principle for specific minimal surfaces.

problem Reflection principle for Jenkins-Serrin type minimal surfaces.
method Proof in homogeneous three-manifolds E(κ,τ)E(κ, τ) for κ≤0 and τ≥0.
result Verification of Schwarz reflection principle for new class of minimal surfaces.

DNNs initially capture low-frequency components before high-frequency ones, a phenomenon called F-Principle.

problem Understanding why DNNs generalize well despite overfitting.
method Empirical study on real and synthetic datasets, focusing on frequency components captured by DNNs.
result DNNs capture dominant low-frequency components first, then high-frequency ones, a phenomenon called F-Principle.

Investigates stability properties of Haezendonck-Goovaerts premium principles in Orlicz spaces.

problem Stability properties of Haezendonck-Goovaerts premium principles in various Orlicz spaces.
method Analysis of stability properties including Fatou and Lebesgue properties, and continuity with respect to ΦΦ-weak convergence.
result Haezendonck-Goovaerts principles satisfy the Fatou property and Lebesgue property under certain conditions.

Study on maximum principles for nonlinear equations on Riemannian manifolds.

problem Investigating strong maximum principles for fully nonlinear equations on Riemannian manifolds.
method Analyzing scaling conditions and applying to various nonlinear operators.
result Established new strong comparison principles for second order uniformly elliptic problems.

Derives Fredholm criteria for isotypical components from a Simonenko principle.

problem Finding Fredholm conditions for isotypical components of invariant pseudodifferential operators.
method General Simonenko's local principle and equivariant local principle for restriction to isotypical components.
result Full proof of equivariant local principle and extension of results.

A pricing principle is introduced for non-attainable claims in incomplete markets.

problem Pricing non-attainable contingent claims in incomplete markets.
method Distorted Radon-Nikodym derivative and Tsallis relative entropy over a family of equivalent martingale measures.
result The pricing principle is closely related to backward stochastic differential equations and is arbitrage-free and time-consistent.

In this paper, we develop a new mathematical technique which allows us to express the joint distribution of a Markov process and its running maximum (or minimum) through the marginal distribution of the process itself. This technique is an extension of the classical reflection principle for Brownian motion, and it is o…

2013-08-09abs ↗pdf ↗

Deep networks often capture low frequency functions, improving generalization.

problem Understanding deep learning's generalization ability.
method Showed F-Principle holds for various loss functions and applied it to differential equations.
result Deep networks capture low frequency functions, leading to better generalization.

New principle for harmonic maps helps study higher-dimensional submanifolds.

problem Understanding unboundedness of totally geodesic projections in higher codimension.
method Introducing a flexible notion of convexity and applying it to harmonic and conformal maps.
result New maximum principle for harmonic maps applicable to various geometric settings.

We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes maximum entropy principle and from standard maximum likelihood estimation.We demonstrate the LME principle BY deriving …

2012-10-19abs ↗pdf ↗

New equivalences found linking parabolicity, comparison principle, and capacity on Riemannian manifolds.

problem Understanding parabolicity and related concepts on Riemannian manifolds.
method Establishing new equivalences between parabolicity, comparison principle, and capacity.
result Equivalence between pp-parabolicity and the comparison principle for the pp-Laplace equation.

Establishes a principle for metric convergence in Kähler geometry.

problem Convergence of evolving Riemannian metrics in Kähler geometry.
method General 'boundedness implies convergence' principle applied to collapsing Calabi-Yau metrics and normalized Kähler-Ricci flows.
result Obtains convergence results for collapsing Calabi-Yau metrics and normalized Kähler-Ricci flows.