The Ramanujan Machine: Automatically Generated Conjectures on Fundamental Constants
arXiv:1907.00205 · doi:10.1038/s41586-021-03229-4
Abstract
Fundamental mathematical constants like and are ubiquitous in diverse fields of science, from abstract mathematics to physics, biology and chemistry. For centuries, new formulas relating fundamental constants have been scarce and usually discovered sporadically. Here we propose a novel and systematic approach that leverages algorithms for deriving mathematical formulas for fundamental constants and help reveal their underlying structure. Our algorithms find dozens of well-known as well as previously unknown continued fraction representations of , , Catalan's constant, and values of the Riemann zeta function. Two example conjectures found by our algorithm and so far unproven are: \begin{equation*} \frac{24}{π^2} = 2 + 7\cdot 0\cdot 1+ \frac{8\cdot1^4}{2 + 7\cdot 1\cdot 2 + \frac{8\cdot2^4}{2 + 7\cdot 2\cdot 3 + \frac{8\cdot3^4}{2 + 7\cdot 3\cdot 4 + \frac{8\cdot4^4}{..}}}} \quad\quad,\quad\quad \frac{8}{7 ζ(3)} = 1\cdot 1 - \frac{1^6}{3\cdot 7 - \frac{2^6}{5\cdot 19 - \frac{3^6}{7\cdot 37 - \frac{4^6}{..}}}} \end{equation*} We present two algorithms that proved useful in finding conjectures: a Meet-In-The-Middle (MITM) algorithm and a Gradient Descent (GD) tailored to the recurrent structure of continued fractions. Both algorithms are based on matching numerical values and thus they conjecture formulas without providing proofs and without requiring prior knowledge on any underlying mathematical structure. This approach is especially attractive for constants for which no mathematical structure is known, as it reverses the conventional approach of sequential logic in formal proofs. Instead, our work supports a different approach for research: algorithms utilizing numerical data to unveil mathematical structures, thus trying to play the role of intuition of great mathematicians of the past, providing leads to new mathematical research.
arXiv comment5 figures, 6 tables, 28 pages including the supplementary information
References in corpus (15)
- Discovering physical concepts with neural networks
- Hamiltonian Neural Networks
- Discovering Symbolic Models from Deep Learning with Inductive Biases
- Learning-Assisted Automated Reasoning with Flyspeck
- Toward an AI Physicist for Unsupervised Learning
- The On-Line Encyclopedia of Integer Sequences
- HOList: An Environment for Machine Learning of Higher-Order Theorem Proving
- The Irrationality Measure of Pi is at most 7.103205334137...
- Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer
- Polynomial Continued Fractions
- One of the Odd Zeta Values from to Is Irrational. By Elementary Means
- Elementary proofs of generalized continued fraction formulae for
- A third-order Apery-like recursion for
- Real Numbers With Polynomial Continued Fraction Expansions
- Normality Analysis of Current World Record Computations for Catalan's Constant and Arc Length of a Lemniscate with
Cited by in corpus (7)
- On scientific understanding with artificial intelligence
- A Triumvirate of AI Driven Theoretical Discovery
- Algorithm-assisted discovery of an intrinsic order among mathematical constants
- Symbolic integration by integrating learning models with different strengths and weaknesses
- Automated conjecturing with \emph{TxGraffiti}
- On the Connection Between Irrationality Measures and Polynomial Continued Fractions
- Criteria for the numerical constant recognition