arXiv (All)
open access
TinyUDE: Solver-Free Universal Differential Equations on Microcontrollers via Lie-Taylor Jet Matching
Training Universal Differential Equations (UDEs) traditionally relies on backpropagating through numerical ODE solvers, creating memory footprints far exceeding the capabilities of edge microcontrollers. We present Lie-Taylor jet matching, a solver-free training framework that fits a hybrid vector f
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
- PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units#1130515
- GeoShrink: Accelerating Diffusion Transformers with Two Lines of Code#1118796
- Diff-NekRS: A Scalable Differentiable Framework for Multi-Timestep Solver-in-the-Loop Training#1048952
- The Frame Kernel Method for Multiscale Operator Learning#820639
- TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning#13088
Record · ID 1063183
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