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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.

168,695 papers · 148 categories

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249499748997 · Jun 202019922001200920172026
48 results for geometric inverse problem

Geometric framework for inverse problems using foliations and dual connections.

problem Reconstruction problems in inverse problems.
method Vaisman foliations and Atiyah--Molino sequences to induce transverse foliations and dual connections.
result Unique, path-independent reconstruction with vanishing torsion and curvature duality.

We develop a geometric version of the inverse problem of the calculus of variations for discrete mechanics and constrained discrete mechanics. The geometric approach consists of using suitable Lagrangian and isotropic submanifolds. We also provide a transition between the discrete and the continuous problems and propos…

2017-08-14abs ↗pdf ↗

GeoFunFlow tackles inverse problems on complex geometries with efficient learning.

problem Challenges in inverse problems governed by PDEs, especially on irregular geometries.
method Combines geometric function autoencoder and latent diffusion model trained via rectified flow.
result Achieves state-of-the-art reconstruction accuracy and efficient inference.

The paper explores solving inverse problems for ODEs with and without constraints.

problem Understanding when second order ODEs can represent Lagrangian models with or without constraints.
method Geometric techniques to address the inverse problem for both constrained and unconstrained systems of second order ODEs.
result The constrained case presents more ambiguities and complexities than the unconstrained one.

Study determines minimal surfaces from boundary data, proving topological and conformal recoverability.

problem Determining minimal surfaces from boundary data.
method Developed a semiclassical nonlinear calculus for complex geometric optics solutions.
result Minimal surfaces can be recovered from the Dirichlet-to-Neumann map under certain conditions.

Researchers solve a formally determined inverse problem in Lorentzian geometry.

problem Determining a Lorentzian metric from boundary measurements of the Dirichlet-to-Neumann map.
method New method using distorted plane wave solutions and geometric, topological, and unique continuation arguments.
result A globally hyperbolic metric agreeing with the Minkowski metric outside a compact set and having the same Dirichlet-to-Neumann map must be the Minkowski metric up to diffeomorphism.

GNPs learn operators on non-Euclidean geometries using neural networks.

problem Learning operators on complex geometries like manifolds.
method Geometric Neural Operators (GNPs) that incorporate geometric properties.
result GNPs can estimate metrics, solve PDEs, and learn LB operators on manifolds.

The Stone-Weierstrass theorem aids in solving inverse problems on specific manifolds.

problem Inverse problems on holomorphically separable Kähler manifolds and conformally transversally anisotropic manifolds.
method Application of the Stone-Weierstrass theorem to show uniqueness in inverse problems.
result Generalization and simplification of earlier results in inverse problems.

In this article we consider the anisotropic Calderon problem and related inverse problems. The approach is based on limiting Carleman weights, introduced in Kenig-Sjoestrand-Uhlmann (Ann. of Math. 2007) in the Euclidean case. We characterize those Riemannian manifolds which admit limiting Carleman weights, and give a c…

2008-03-25abs ↗pdf ↗

New method speeds up Bayesian inverse problem solving with neural operators.

problem Solving infinite-dimensional Bayesian inverse problems with high computational cost.
method Delayed-acceptance geometric MCMC driven by derivative-informed neural operator surrogates.
result Significant speedup in generating posterior samples (3-9 times faster).

We introduce a method for solving Calderón type inverse problems for semilinear equations with power type nonlinearities. The method is based on higher order linearizations, and it allows one to solve inverse problems for certain nonlinear equations in cases where the solution for a corresponding linear equation is not…

2019-03-29abs ↗pdf ↗

The article resolves complex structures in transport twistor spaces, proving a Newlander-Nirenberg theorem.

problem Degenerate complex structures in transport twistor spaces.
method Holomorphic blow-down structure maps to resolve degeneracy and gain insight into complex geometry.
result Global and local β-maps for various metrics, proving a Newlander-Nirenberg theorem for degenerate complex structures.

Study inverse boundary value problem for Monge-Ampère equation on convex domains.

problem Determine a positive source function from the Dirichlet-to-Neumann map for Monge-Ampère equation.
method Recover Hessian as Riemannian metric, prove DN map uniqueness, develop asymptotic expansions, solve nonlocal \overline{\partial}-equation.
result DN map uniquely determines positive source function in convex Euclidean plane domains.

Geometrically studies Moore-Penrose inverse and polar decomposition continuity.

problem Continuity of Moore-Penrose inverse for perturbations by operator ideals.
method Geometric construction using essential codimension and Banach-Lie group action.
result Moore-Penrose inverse is a real analytic map between manifolds.

This paper sets a lower bound for sample complexity in inverse reinforcement learning.

problem Finding a reward function that generates a desired optimal policy in MDPs.
method Information-theoretic lower bound using geometric construction and Fano's inequality.
result An O(nlogn)O(n \log n) sample complexity lower bound for IRL problems.

Researchers reconstruct simple Riemannian manifolds from boundary wave arrival times.

problem Reconstructing Riemannian manifolds from unknown interior sources and arrival times.
method Discrete metric approximation using labeled Gromov--Hausdorff distance.
result Finite-time approximations converge to the true Riemannian manifold.

Deep learning solves wave-based inverse problems, including super-resolution imaging.

problem Solving inverse wave scattering problems across all length scales.
method Wide-band butterfly network coupled with dynamic noise injection.
result Framework successfully solves super-resolution imaging problems.

We prove uniqueness results for a Calderon type inverse problem for the Hodge Laplacian acting on graded forms on certain manifolds in three dimensions. In particular, we show that partial measurements of the relative-to-absolute or absolute-to-relative boundary value maps uniquely determine a zeroth order potential. T…

2013-10-17abs ↗pdf ↗

Researchers recover Riemannian manifolds and lower order terms from travel time data.

problem Recovering Riemannian manifolds and lower order terms from travel time data.
method Adaptation of the Boundary Control method to recover lower order terms.
result Complete Riemannian manifolds and lower order terms can be uniquely recovered from a local source to solution map.

The paper studies area-preserving and length-preserving inverse curvature flow for planar curves with singularities.

problem Investigating the evolution of planar curves with singularities under area-preserving and length-preserving inverse curvature flow.
method Area-preserving and length-preserving inverse curvature flow for \ell-convex Legendre curves.
result The flow results in a circle for \ell-convex Legendre curves, providing geometric inequalities.

Study uses machine learning to optimize seismic design parameters.

problem Optimizing seismic design parameters for performance-based design.
method Implementing explainable machine learning models to map design variables and performance metrics, integrated into a genetic optimization algorithm.
result Highly accurate surrogate models (R2> 90%) across diverse building types and hazards, identifying optimal member properties.

Study of curve evolution in 2D space forms converging to a circle.

problem Understanding curve evolution in 2D space forms.
method Inverse curvature flow with normal speed defined by weighted inverse curvature and support function.
result Solutions exist for all time and converge exponentially to a standard round geodesic circle.