ReviewArchives of computational methods in engineering : state of the art reviews2023
Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives.
Review in Archives of computational methods in engineering : state of the art reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hybrid Intelligence for Fouling Prediction and Adaptive Clean-in-Place Optimization in Food Processing Heat Exchangers: A Systematic Review.Journal of food science · 2026Pooled it
- Domain Knowledge Constrained Symbolic Regression for Optimising Dermal Drug Formulations.Pharmaceutical research · 2026Article
- Moving from table to graph in physics-informed spatio-temporal symbolic regression.Scientific reports · 2026Article
- ASP-assisted symbolic regression for interpretable modelling of 3D laminar channel flow.Scientific reports · 2026Article
- Interpretable Machine Learning to Understand Wildfire Toxicity: Bridging Chemicals, Omics, and Toxicological Outcomes via Symbolic Regression with Novel Feature Scoring.Chemical research in toxicology · 2026Article
- Unraveling Fundamental Activity-Stability Relationships in Rutile Oxides.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Article
- physically interpretable residual strength prediction of corroded pipelines via symbolic Bayesian networks.Scientific reports · 2026Article
- Explainable and interpretable models for predicting early-onset hypertension in the Tlalpan 2020 cohort.Frontiers in digital health · 2026Article
- Article
- Mechanism-aware and explainable artificial intelligence for drug release prediction in polymeric drug delivery systems.Frontiers in medicine · 2026Article
- The role of digital twins in P4 medicine: A paradigm for modern healthcare.NPJ digital medicine · 2025Review
- LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data.Briefings in bioinformatics · 2025Article
- Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.Nanophotonics (Berlin, Germany) · 2025Review
- Dynamics of an epidemic controlled by isolation and quarantine: A probability-based deterministic model.Infectious Disease Modelling · 2025Article
- Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025Article
- Physically Consistent Self-Diffusion Coefficient Calculation with Molecular Dynamics and Symbolic Regression.International journal of molecular sciences · 2025Article
- Using graph neural network and symbolic regression to model disordered systems.Scientific reports · 2025Article
- Article
- Automated data-driven discovery of material models based on symbolic regression: A case study on the human brain cortex.Acta biomaterialia · 2024Article
- Optimization Design of Submerged-Entry-Nozzle Structure Using NSGA-II Genetic Algorithm in Ultra-Large Beam-Blank Continuous-Casting Molds.Materials (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s11831-023-09922-z.
Identifiers
What OpenQuestion holds
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.