Project Launch: Unleashing WiSARDINES (PI-WNN)
I'm incredibly excited to lock in the initial release of WiSARDINES (Physics-Informed Weightless Neural Network Engine) for the Global Innovation Build Challenge!
Instead of throwing massive, power-hungry deep learning models at physics problems, I built a framework that teaches physical boundaries directly to weightless, bit-packed lookup tables running on tiny edge hardware.
What's packed into this release:
embedded_core.c: A dependency-free, strict C99 core optimized for zero-overhead execution loops on microcontrollers.- Cuckoo Table Address Storage: Solved the exponential RAM footprint growth of weightless logic using optimized sparse dictionary mapping.
- Symmetry & Invariant Constraints: Hard-masking for physical boundaries (like mass tiers) and soft-score logical priors using Laplace smoothing.
- Empirical Validation: Proven track records streaming the 1-million-row UCI HIGGS physics archive.
The complete codebase, architectural profiles, and simulation scripts are live on GitHub. Take a look under the hood: https://github.com/t4rxnn-devel/WiSARDINES-Physics-Informed-Weightless-Neural-Network-PI-WNN-
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