Analysis & PDE · Mathematics & Statistics
Differential Geometry
The newest papers in Differential Geometry that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
22 papers, from 7 September 2026 to 5 November 2026: 22 arXiv preprints.
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Newest papers
Data-driven discovery of intrinsic Lie point symmetries of differential equation systems
Discovering continuous symmetries directly from differential-equation data can reveal geometric structure for model reduction and scientific machine learning without symbolic determining equations…On a Class of Non-Local Elliptic-Transport Systems in Score-Guided Probability Flow on $SE(3)$: Score-Singularity Resolution, Space-Time ADN Shift, and Markov Mean-Field Games
We establish mathematical foundations for optimal spatiotemporal vector fields governing continuous score-guided probability flows over bounded submanifolds $M \subset SE(3)$ on the Special…The Continuum Limit of the Deep Linear Network
The Deep Linear Network (DLN) is a simple version of a Multilayer Perceptron (MLP) in which the activation function is the identity and the bias vector is the zero vector.Einstein Metrics and Quantum Control on Symmetric Tensor Product Quotients
Let $M_n=(\mathrm{PU}(2)^n\rtimes S_n)\backslash\mathrm{PU}(2^n)$ be the space of projective $n$-qubit unitaries modulo local unitaries and permutations of the qubits.The geometry of Floquet states
This paper studies the moduli space of "good" periodic Hamiltonians, i.e., periodic Hamiltonians with nondegenerate Floquet spectrum.The Geometry of Manifold Learning: Singular foliations via Deep Learning models
Deep learning algorithms have become widely used and successful across a wide range of tasks.Geometric aspects of fault tolerance
We highlight three geometric aspects of fault tolerance.SympFormer: Inertial Attention Blocks from Damped Hamiltonian Dynamics on Spaces of Probability Measures
The attention mechanism underpins modern large language models, yet their dynamics remain only partially understood.Learning Normal Coordinates on Riemannian Manifolds with Flow Charts
Riemannian manifolds are powerful models for problems with non-Euclidean structure.Computing Fréchet Means on Unknown Submanifolds of Riemannian Manifolds
Empirical averages are fundamental statistical primitives.Intrinsic Geometry Matching: learning observation-equivariant process geometry
Real data are often arbitrary, indirect observations of an unknown underlying process, and those observations can distort its geometry.Testing Algebraic Complete Intersections
Given independent and identically distributed samples samples from a probability distribution in a potentially high-dimensional real space, we study the problem of testing whether the distribution…Coordinate-Free Automatic Differentiation
Automatic differentiation (AD) is a cornerstone of modern machine learning.Riemannian Shape Analysis of the Corpus Callosum in Kendall Space: Aging and Alzheimer's Disease
The corpus callosum (CC) is a major white-matter structure and a well-established marker of brain aging, but most studies quantify it using scalar summaries that discard its boundary geometry.Activation Flow: Manufacturing Activations for Steering
Difference-in-means steering requires activations recorded while a model shows the desired behavior, which a sandbagging model withholds by deliberately underperforming.Riemannian Structure and Optimization for a Class of Low-Parametric Orthogonal Matrices
In this paper, we are concerned with matrices formed by block-diagonal factors interleaved with fixed permutations -- a flexible family of structured matrices.Theory for groupoid equivariant neural networks: an approach for steerable CNNs on bounded domains
Equivariant convolutional neural networks are usually built from a group acting globally on the space of signals.CurvFlow-DTA: dual-graph discrete Ricci curvature flow for drug--target affinity prediction
Graph neural networks are widely used for drug--target affinity (DTA) prediction, and discrete Ricci curvature has recently been used to characterize molecular graph geometry.Special Lagrangian cones in Deep Learning
We introduce a matrix generalization of the cone of Harvey and Lawson and prove that it is an exact special Lagrangian manifold.Optimal Pruning for Neural Architectures using Fisher Information Distances
A new scheme for parameter pruning is introduced, derived from the differential-geometric distance in model space.