arXiv (All)
open access
Unifying Physical Backpropagation
Physical computing systems exploit device dynamics for computation, but their gradient-based optimization is challenging: backpropagation through a digital twin suffers from a model-reality gap. On-device gradient computation could resolve this issue, and a handful of theoretical and experimental st
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
- Training Hamiltonian neural networks without backpropagation#144234
- AutoPDEBench: Benchmarking LLM Auto-Research for Neural PDE Solver Design#1116576
- Fast simulation of nonlinear deep resistive networks for energy-based computation#984283
- Kairos: A Scalable Serving System for Physical AI#178851
- NeuroFlex: Lossless Element-Level ANN-SNN Co-Execution for Efficient Sparse Inference#1013696
Record · ID 1062983
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