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SciML for Knowledge Discovery

Reference implementation of our ICARCV 2022 paper — a head-to-head comparison of four symbolic regression methods on the Feynman-03 and Nguyen-12 benchmarks, with an honest look at where each one breaks.

Venue
ICARCV 2022
Type
Paper + code
Datasets
Feynman-03, Nguyen-12
Stack
Jupyter / Python
Repository
sciml · ICARCV 2022 paper

Official implementation of A Comparative Study on Machine Learning Algorithms for Knowledge Discovery, accepted at the 17th International Conference on Control, Automation, Robotics and Vision (ICARCV 2022) at Nanyang Technological University.

What the paper does

It surveys the dominant approaches to symbolic regression — the family of methods that try to recover a closed-form equation from observations — and benchmarks them under matched conditions. The goal is to understand the strengths and limitations of each method honestly, and to call out where the field still has open problems.

Methods compared

  • Genetic Programming (GPL) — gplearn as the implementation.
  • Deep Symbolic Regression (DSR) — RL-based search over the symbolic space.
  • AI-Feynman (AIF) — Heuristic search using recurring patterns from physics formulas.
  • Neural Symbolic Regression that Scales (NeSymRes) — Pretrained transformer over 100M synthetic equations.

Datasets

  • Feynman-03 — 52 equations sampled from the AI-Feynman dataset, capped at three input variables.
  • Nguyen-12 — 12 equations with up to two input variables, intentionally including high-frequency terms like x^5 and x^6 to stress-test the methods.

The repo contains the full benchmark harness, the noise-sweep experiments shown in the paper, and the citation block if you find it useful.