How are Scientific Concepts Birthed? Typing Rules of Concept Formation in Theoretical Physics Reasoning
Not on the map. Why?
Abstract (arXiv)
This work aims to formalize some of the ways scientific concepts are formed in the process of theoretical physics discovery. Since this may at first seem like a task beyond the scope of the exact sciences (natural and formal sciences), we begin by presenting arguments for why scientific concept formation can be formalized. Then, we introduce type theory as a natural and well-suited framework for this formalization. We formalize what we call "ways of discovering new concepts" including concept distinction, property preservation, and concept change, as cognitive typing rules. Next, we apply these cognitive typing rules to two case studies of conceptual discovery in the history of physics: Einstein's reasoning leading to the impossibility of frozen waves, and his conceptual path to the relativity of time. In these historical episodes, we recast what a physicist might informally call "ways of discovering new scientific concepts" as compositional typing rules built from cognitive typing rules - thus formalizing them as scientific discovery mechanisms. Lastly, we computationally model the type-theoretic reconstruction of Einstein's conceptual path to the relativity of time as a program synthesis task.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 62 references that are in the map. First posted on arXiv 12 Sep 2025.
Builds on
12 of 62 references.
References · 12
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
- 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
- 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
- 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 548
- 2017Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanicscited by 214
- 2020AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularitycited by 144
- 2023We're Afraid Language Models Aren't Modeling Ambiguitycited by 126
- 2022Peano: Learning Formal Mathematical Reasoningcited by 15
- 2023A Transformer Model for Symbolic Regression towards Scientific Discoverycited by 9
- 2020From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoningcited by 7
- 2024Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligencecited by 5
- 2022Type theory in human-like learning and inferencecited by 3
Built on by
Not cited yet.