mlsanityData to 14 Oct 2026

Symbolic regression for scientific discovery: an application to wind speed forecasting

arXiv 2102.10570 ↗First posted 21 Feb 2021cs.LG physics.ao-phcited by 16

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Abstract (arXiv)

Symbolic regression corresponds to an ensemble of techniques that allow to uncover an analytical equation from data. Through a closed form formula, these techniques provide great advantages such as potential scientific discovery of new laws, as well as explainability, feature engineering as well as fast inference. Similarly, deep learning based techniques has shown an extraordinary ability of modeling complex patterns. The present paper aims at applying a recent end-to-end symbolic regression technique, i.e. the equation learner (EQL), to get an analytical equation for wind speed forecasting. We show that it is possible to derive an analytical equation that can achieve reasonable accuracy for short term horizons predictions only using few number of features.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 10 of its 46 references that are in the map. First posted on arXiv 21 Feb 2021.

Builds on

10 of 46 references.

References · 10

  1. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  2. 2020Discovering Symbolic Models from Deep Learning with Inductive Biasescited by 344
  3. 2015Visualizing Deep Convolutional Neural Networks Using Natural Pre-Imagescited by 93
  4. 2019Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discoverycited by 93
  5. 2020SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecturecited by 71
  6. 2021Broad-UNet: Multi-scale feature learning for nowcasting taskscited by 25
  7. 2021Deep Graph Convolutional Networks for Wind Speed Predictioncited by 19
  8. 2020Wind speed prediction using multidimensional convolutional neural networkscited by 17
  9. 2020Deep coastal sea elements forecasting using U-Net based modelscited by 14
  10. 2020Deep brain state classification of MEG datacited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 3; 2025: 4; 2026: 3.

16 papers.

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  1. 2022Interpretable Scientific Discovery with Symbolic Regression: A Reviewcited by 105
  2. 2022Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discoverycited by 46
  3. 2022AA-TransUNet: Attention Augmented TransUNet For Nowcasting Taskscited by 22
  4. 2021Multistream Graph Attention Networks for Wind Speed Forecastingcited by 12
  5. 2024Training Stiff Neural Ordinary Differential Equations with Implicit Single-Step Methodscited by 11
  6. 2021TENT: Tensorized Encoder Transformer for Temperature Forecastingcited by 7
  7. 2024SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compressioncited by 7
  8. 2023Bayesian polynomial neural networks and polynomial neural ordinary differential equationscited by 6
  9. 2024Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methodscited by 5
  10. 2025On the definition and importance of interpretability in scientific machine learningcited by 5
  11. 2025Discovering equations from data: symbolic regression in dynamical systemscited by 2
  12. 2025Can Test-time Computation Mitigate Reproduction Bias in Neural Symbolic Regression?cited by 1
4 more
  1. 2025From Embeddings to Equations: Genetic-Programming Surrogates for Interpretable Transformer Classificationcited by 1
  2. 2026SmaAT-QMix-UNet: A Parameter-Efficient Vector-Quantized UNet for Precipitation Nowcastingcited by 0
  3. 2026A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regionscited by 0
  4. 2026Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfallcited by 0