mlsanityData to 14 Oct 2026

Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data

arXiv 1612.08544 ↗First posted 27 Dec 2016cs.LG cs.AI stat.MLcited by 171

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Abstract (arXiv)

Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.

Key facts. Cited by 171 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Dec 2016.

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Citing papers per year: 2021: 42; 2022: 30; 2023: 24; 2024: 19; 2025: 33; 2026: 23.

171 papers.

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  1. 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 130
  2. 2022When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learningcited by 65
  3. 2021Learning in Sinusoidal Spaces with Physics-Informed Neural Networkscited by 62
  4. 2021Encoding physics to learn reaction-diffusion processescited by 61
  5. 2021Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modelingcited by 59
  6. 2021Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEscited by 43
  7. 2024A Survey of Deep Learning and Foundation Models for Time Series Forecastingcited by 33
  8. 2021Constructing Neural Network-Based Models for Simulating Dynamical Systemscited by 31
  9. 2021Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approachescited by 30
  10. 2023Learning stiff chemical kinetics using extended deep neural operatorscited by 30
  11. 2022How important are activation functions in regression and classification? A survey, performance comparison, and future directionscited by 29
  12. 2021Towards a Collective Agenda on AI for Earth Science Data Analysiscited by 27
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  1. 2023Physics-informed neural networks for solving forward and inverse problems in complex beam systemscited by 24
  2. 2021PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physicscited by 21
  3. 2022EINNs: Epidemiologically-informed Neural Networkscited by 21
  4. 2021MC-LSTM: Mass-Conserving LSTMcited by 17
  5. 2021Physically Consistent Neural Networks for building thermal modeling: theory and analysiscited by 17
  6. 2021Finite Volume Neural Network: Modeling Subsurface Contaminant Transportcited by 15
  7. 2021Explainable Machine Learning with Prior Knowledge: An Overviewcited by 15
  8. 2021Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrologycited by 15
  9. 2021On Recurrent Neural Networks for learning-based control: recent results and ideas for future developmentscited by 14
  10. 2023A spectrum of physics-informed Gaussian processes for regression in engineeringcited by 14
  11. 2024Knowledge-guided Machine Learning: Current Trends and Future Prospectscited by 14
  12. 2022Integration of knowledge and data in machine learningcited by 13
  13. 2022Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solverscited by 12
  14. 2024Geometry-Informed Neural Networkscited by 12
  15. 2023Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlookcited by 11
  16. 2023Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applicationscited by 11
  17. 2025Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunitiescited by 11
  18. 2021Surrogate Modeling of Fluid Dynamics with a Multigrid Inspired Neural Network Architecturecited by 9
  19. 2021A Review of Physics-based Machine Learning in Civil Engineeringcited by 9
  20. 2022Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Surveycited by 9
  21. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
  22. 2021CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1cited by 8
  23. 2022Physics-informed machine learning for Structural Health Monitoringcited by 8
  24. 2023Origin-Destination Network Generation via Gravity-Guided GANcited by 8
  25. 2023Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulationscited by 8
  26. 2025From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversioncited by 8
  27. 2021A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processescited by 7
  28. 2022A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacousticcited by 7
  29. 2022Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of datacited by 7
  30. 2023Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directionscited by 7
  31. 2024Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolutioncited by 7
  32. 2024CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilationcited by 7
  33. 2025Learning mechanical systems from real-world data using discrete forced Lagrangian dynamicscited by 7
  34. 2025Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Modelscited by 7
  35. 2021Model-data-driven constitutive responses: application to a multiscale computational frameworkcited by 6
  36. 2021Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problemscited by 6
  37. 2023Real-time simulation of viscoelastic tissue behavior with physics-guided deep learningcited by 6
  38. 2023Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equationscited by 6

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