arXiv (All)
open access
Universal Approximation of Nonlinear Operators and Their Derivatives
artificial-intelligencefunctional-analysismachine-learningnumerical-analysisoptimization-and-control
Establishing Universal Approximation Theorems (UATs) for nonlinear operators and their derivatives is a foundational open problem in Operator Learning (OL) and raises delicate questions in Nonlinear Functional Analysis. We prove the first UATs for $k$-times differentiable nonlinear operators and the
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces#343499
- Toward nonlinear representations with Gaussian-splat manifolds for physics-informed learning.#785403
- Gradient-based linear and nonlinear dimension reduction#796302
- Learning Lyapunov Operators for Nonlinear Systems#1013051
- Learning Lyapunov Operators for Nonlinear Systems#965411
Record · ID 821449
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive