Symbolic Regression

Latest papers 134

19 papers in the last four weeks, up 58% on the four weeks before. 0.1% of all new papers.

Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization
Jun 29, 2026Hao Xu, Siyu Lou, Yuntian Chen +1PDE Discovery Symbolic Regression
Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang 315200, China · Department of Electrical Engineering, Tsinghua University, Beijing 100084, P. R. China. · Shanghai Jiao Tong University, School of Computer Science, Shanghai, China +1
Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees
Jun 28, 2026Şuayp Talha Kocabay, Talha Rüzgar Akkuş, Kerem YalçınSample Complexity Statistical Learning Theory
Independent Researcher · TÜBİTAK Science High School
LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search
Jun 23, 2026Nikhil Abhyankar, Sha Li, Sanchit Kabra +3Nonlinear System Identification System Identification
Virginia Tech
Evolutional Math: Cross-Validated Island-Model Genetic Programming for Interpretable Symbolic Regression on Small, Wide Datasets
Jun 20, 2026Artem AndrianovGenetic Programming Symbolic Regression
Cyntegrity Germany GmbH, Hofheim am Taunus, Germany · Independent Researcher, Muensterer Str. 49, 65719 Hofheim am Taunus, Germany
Agentic Symbolic Search: Characterizing PDEs Beyond Hand-crafted Expressions, Meshes, and Neural Networks
Jun 18, 2026Zongmin Yu, Liu YangAI Agents for Scientific Discovery PDE Solving
National University of Singapore
NOVA: Symbolic Regression Discovery of Interpretable Car-Following and Lane-Change Models with Driver Heterogeneity
Jun 9, 2026Ishak Abassi, Nassim Ali Bouazzouni, Farah Ibelaiden +1Intelligent Transportation Systems Vehicle Trajectory Prediction
Department of Industrial Systems Engineering, National School of Artificial Intelligence (ENSIA), Algeria. · Cosys-Grettia, Univ Gustave Eiffel, F-77454 Marne-la-Vallee, France.
Minimalist Genetic Programming
Jun 8, 2026Leonardo TrujilloGenetic Programming Program Synthesis
Tecnol´ogico Nacional de M´exico/IT de Tijuana,Tijuana, BC, Me xico · LASIGE, Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
Data-driven discovery of governing differential equations across physical systems
Jun 8, 2026Siyu Lou, Hao Xu, Wenguan Wang +6PDE Discovery Autonomous Scientific Discovery
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China · Ningbo Key Laboratory of Advanced Manufacturing Simulation, Eastern Institute of Technology, Ningbo, Ningbo, China · The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China. +5
ERBench: A Benchmark and Testsuite for Equation Discovery Algorithms
Jun 8, 2026Paul Kahlmeyer, Henrik Voigt, Michael Habeck +1Benchmark Design Scientific Discovery
Department of Computer Science University of Jena Inselplatz 5, 07743 Jena, Germany
Compile Once, Differentiate Everywhere: A Differentiable Meta-Circular Interpreter
Jun 7, 2026Lucas ShenemanAutomatic Differentiation Program Synthesis
Institute for Interdisciplinary Data Sciences University of Idaho
EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification
Jun 6, 2026Da Li, Xinxin Li, Xingyu Cui +3Symbolic Regression
Academy for Advanced Interdisciplinary Studies, Northeast Normal University, Changchun, 130024, Jilin, China · School of Mathematical Sciences, East China Normal University, Shanghai, 200241, Shanghai, China · Shanghai Zhangjiang Institute of Mathematics, Shanghai, 201203, Shanghai, China +3
Discovering Multiscale Deep Formulas in Complex Systems via Neural-Guided Lambda Calculus
Jun 5, 2026Hanqiao Yu, Shusen Yang, Xuebin Ren +1AI for Science Symbolic Regression
National Engineering Laboratory for Big Data Analytics, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China. · School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China. · School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China.
A Data-Free Symbolic Regression Approach for Solving Equations
Jun 5, 2026Sergei Garmaev, Vinay Sharma, Olga FinkPDE Solving Symbolic Regression
Intelligent Maintenance and Operations Systems Laboratory, EPFL, Lausanne, CH-1015, Switzerland
FunctionEvolve: Structure-Guided Symbolic Regression with LLMs
Jun 5, 2026Zeyu Xia, Jun Zhu, Dong YanGenetic Programming Large Language Model-Guided Scientific Discovery
Bosch Center for Artificial Intelligence · Department of Computer Science and Technology, Tsinghua University · Department of Computer Science and Technology +1
Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression
Jun 4, 2026Julia Reuter, Fabricio Olivetti de FrancaUncertainty Quantification Symbolic Regression
Center for Mathematics, Computation and Cognition (CMCC), Federal University of ABC, Brazil
EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning
Jun 4, 2026Sota AsanumaStructural Causal Models Causal Structure Learning
SoftBank Corp.
Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
Jun 3, 2026Xinyu Pang, Zhanke Zhou, Xuan Li +5Large Language Model-Guided Optimization Large Language Model-Guided Scientific Discovery
Beijing National Research Center for Information Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing, P.R. China · TMLR Group, Department of Computer Science, Hong Kong Baptist University · Lenovo Research
Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression
Jun 1, 2026Johannes Koch, Tanja Alderliesten, Peter A. N. BosmanGenetic Programming Genetic Algorithms
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands · Delft University of Technology, Delft, The Netherlands · Leiden University Medical Center, Leiden, The Netherlands
Data Enrichment for Symbolic Regression Using Diffusion Models
May 31, 2026Simon De Reuver, Tamas Kristof Toth, Teddy LazebnikPhysics-Informed Diffusion Models Physics-Informed Generative Modeling
Department of Computing, Jönköping University, Jönköping, Sweden · Department of Information Science, University of Haifa, Haifa, Israel
Discovering Thermodynamically Admissible Dissipation Potentials via Grammar-Based Symbolic Regression
May 29, 2026Federico Califano, Jacopo CiambellaMaterials Science Computational Materials Science
Learning Parametric Nitrogen Fertilizer Response Curves Using Neuro Symbolic Regression
May 29, 2026Giorgio Morales, John SheppardPrecision Agriculture Symbolic Regression
Aston Centre for Artificial Intelligence Research and Application, Aston University, Birmingham, UK · Gianforte School of Computing, Montana State University, Bozeman, US
GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis
May 29, 2026Jasper Post, Johannes Koch, Anton Bouter +2GPU Acceleration Evolutionary Optimization
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands · Delft University of Technology, Delft, The Netherlands · Leiden University Medical Center, Leiden, The Netherlands
Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
May 27, 2026Evgeny S. Saveliev, Samuel Holt, Nabeel Seedat +3Genomics Large Language Model-Guided Scientific Discovery
University of Cambridge · Thomson Reuters Foundational Research · U. Colorado, Anschutz Medical Campus +1
Symbolic Regression via Latent Iterative Refinement
May 26, 2026Xieting Chu, Sriram Vishwanath, Vijay GaneshLatent Variable Models Amortized Inference
Georgia Institute of Technology
When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
May 22, 2026Boxiao Wang, Kai Li, Zhiwei Chen +5Scientific Discovery Symbolic Regression
Institute of Automation, Chinese Academy of Sciences
Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions
May 21, 2026Gabriel Kronberger, Fabricio Olivetti de Franca, Deaglan J. Bartlett +2Minimum Description Length Genetic Programming
Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 11, 4232 Hagenberg, Austria · Center of Mathematics, Computing and Cognition Federal University of ABC Av. dos Estados 5001, Santo Andre, 09280-560, SP, Brazil · Astrophysics, University of Oxford Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK +1
STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery
May 18, 2026Jiarui Su, Songjun Tu, Bei Sun +1LLM Self-Refinement AI Agents for Scientific Discovery
Central South University · Institute of Automation, Chinese Academy of Sciences · Pengcheng Laboratory
Diversified Residual Symbolic Regression
May 15, 2026Koki Ikeda, Masahiro Nomura, Ryoki HamanoSymbolic Regression
CyberAgent Shibuya, Tokyo, Japan · Institute of Science Tokyo Meguro, Tokyo, Japan
FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression
May 12, 2026Zhiming Yu, Wangtao Lu, Xin LaiNonlinear System Identification Automated Feature Engineering
School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, China · Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
A Comparative Study of Model Selection Criteria for Symbolic Regression
May 11, 2026Ali Soltani, Gabriel Kronberger, Fabricio Olivetti de Franca +2Model Selection Symbolic Regression
Aarhus University Department of Mechanical Engineering Aarhus, Denmark · Heuristic and Evolutionary Algorithms Laboratory (HEAL) University of Applied Sciences Upper Austria Hagenberg, Austria · Federal University of ABC Center for Mathematics, Computing, and Cognition Brazil +1

Jun 29, 2026J/K move · Enter open · S save

Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization

Hao Xu, Siyu Lou, Yuntian Chen, Dongxiao Zhang

Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang 315200, China · Department of Electrical Engineering, Tsinghua University, Beijing 100084, P. R. China. · Shanghai Jiao Tong University, School of Computer Science, Shanghai, China+1

PDE Discovery Symbolic Regression

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Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning. Current data-driven approaches typically operate on a single dataset, inherently limiting their performance when faced with restricted observations. In practice, multiple datasets are often available for the same physical system, distinguished only by distinct initial conditions or boundary configurations. Here, we present a competitive optimization framework designed to discover shared partial differential equations (PDEs) from multi-source datasets, termed MCO-PDE. The framework first trains independent neural surrogates for each data source, and then employs a soft-competitive weighting mechanism to dynamically assess dataset credibility and aggregate a consensus global coefficient. Integrated with a genetic algorithm for structural search, this approach simultaneously identifies the functional forms and parameters of the governing laws. We demonstrate that fusing as few as 50 observations per dataset across seven cases recovers canonical equations with high accuracy. The framework inherently handles two- and three-dimensional domains characterized by irregular boundaries and heterogeneous coefficients, and successfully extracts physically meaningful laws from real-world wave-tank experiments. Overall, this work establishes a promising route for automated scientific discovery via heterogeneous data fusion.