Polynomial ODEs
Cross-source consensus on Polynomial ODEs from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Risks & contraindications
Comparisons
Highlighted claims
- Uniform threshold-affine normal forms can be represented by robust polynomial ODEs using polynomial clocks, comparators, sample-and-hold transport, and shadowing. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- Polynomial ODEs can supply rounding, comparators, clocks, stabilization, and error correction without ReLU gates or external discrete branching. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- Robust polynomial ODE computation uses a fixed rational polynomial vector field with primitive recursive observation and safety bounds. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- The polynomial ODE construction uses a rational polynomial clock with sine-cosine style coordinates and period 2π. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- The polynomial ODE construction depends on robust continuous-time mechanisms and primitive recursive observation and safety bounds. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs