Papers

• Riemannian Deep Learning:Modules, Networks, and Geometries (2026)
  Chen Ziheng
  4.33
• A user's guide to PINNs in geometric analysis: lessons from the asymptotic Plateau problem (2026)
  Tancredi Schettini Gherardini
  1.94
• Elastic Curves via Geometric Mechanics (2026)
  Oliver Gross et al.
  1.94
• Kernel-Gradient Drifting Models (2026)
  Maria Esteban-Casadevall et al.
  1.83
• Accelerate Vector Diffusion Maps by Landmarks (2026)
  Sing-Yuan Yeh et al.
  1.72
• Tensor Gauge Flow Models (2025)
  Alexander Strunk et al.
  1.50
• Carr\'e du champ flow matching: better quality-generalisation tradeoff in generative models (2025)
  Jacob Bamberger et al.
  1.44
• Hessian Geometry of Latent Space in Generative Models (2025)
  Alexander Lobashev et al.
  1.22
• Ergodic Generative Flows (2025)
  Leo Maxime Brunswic et al.
  1.17
• Manifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian Geometry (2025)
  Willem Diepeveen et al.
  1.17
• Deep Generative Models: Complexity, Dimensionality, and Approximation (2025)
  Kevin Wang et al.
  1.11
• Geometric Generative Models based on Morphological Equivariant PDEs and GANs (2024)
  El Hadji S. Diop et al.
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• Pullback Flow Matching on Data Manifolds (2024)
  Friso de Kruiff et al.
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