ArticleScience advances2020
A Bayesian machine scientist to aid in the solution of challenging scientific problems.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed.
- Data-driven discovery of digital twins in biomedical research.Briefings in bioinformatics · 2026Review
- Discovering physical laws with parallel symbolic enumeration.Nature computational science · 2026Article
- Article
- The entities enabling scientific fraud at scale are large, resilient, and growing rapidly.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Sustainable Development Goals-Based Prospective Process Design Using Hybrid Modeling.Industrial & engineering chemistry research · 2025Article
- Automating the practice of science: Opportunities, challenges, and implications.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Human mobility is well described by closed-form gravity-like models learned automatically from data.Nature communications · 2025Article
- Multi-Objective Optimization of a Hybrid Fossil/Renewable Carbon Methanol Cluster.ACS sustainable chemistry & engineering · 2025Article
- Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models.Machine learning · 2025Article
- Evolving scientific discovery by unifying data and background knowledge with AI Hilbert.Nature communications · 2024Article
- Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Using machine learning to find exact analytic solutions to analytically posed physics problems.Heliyon · 2024Article
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
- Distilling identifiable and interpretable dynamic models from biological data.PLoS computational biology · 2023Article
- Review
- Data, measurement and empirical methods in the science of science.Nature human behaviour · 2023Review
- AI for identifying social norm violation.Scientific reports · 2023Article
- Dimensionless Groups by Entropic Similarity: I - Diffusion, Chemical Reaction and Dispersion Processes.Entropy (Basel, Switzerland) · 2023Article
- Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev-Zeldovich flux-mass scatter.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Fundamental limits to learning closed-form mathematical models from data.Nature communications · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need "machine scientists" that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.