ArticleFrontiers in artificial intelligence2020
Discovery of Physics From Data: Universal Laws and Discrepancies.
Article in Frontiers in artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- Data-Driven Volumetric Computed Tomography Image Generation From Surface Structures Using a Patient-Specific Deep Leaning Model.International journal of radiation oncology, biology, physics · 2025Article
- The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems.Journal of personalized medicine · 2024Review
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- Deep Learning in the Ubiquitous Human-Computer Interactive 6G Era: Applications, Principles and Prospects.Biomimetics (Basel, Switzerland) · 2023Review
- Sparse identification of Lagrangian for nonlinear dynamical systems via proximal gradient method.Scientific reports · 2023Article
- A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge.Scientific reports · 2023Article
- Self-Supervised Keypoint Discovery in Behavioral Videos.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2022Article
- Bayesian uncertainty quantification for data-driven equation learning.Proceedings. Mathematical, physical, and engineering sciences · 2021Article
- Deep learning of contagion dynamics on complex networks.Nature communications · 2021Article
- Sparse nonlinear models of chaotic electroconvection.Royal Society open science · 2021Article
- Data-Driven Discovery of Mathematical and Physical Relations in Oncology Data Using Human-Understandable Machine Learning.Frontiers in artificial intelligence · 2021Article
- SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
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4 authors.
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Abstract
Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone. However, positing a universal physical law from data is challenging without simultaneously proposing an accompanying discrepancy model to account for the inevitable mismatch between theory and measurements. By revisiting the classic problem of modeling falling objects of different size and mass, we highlight a number of nuanced issues that must be addressed by modern data-driven methods for automated physics discovery. Specifically, we show that measurement noise and complex secondary physical mechanisms, like unsteady fluid drag forces, can obscure the underlying law of gravitation, leading to an erroneous model. We use the sparse identification of non-linear dynamics (SINDy) method to identify governing equations for real-world measurement data and simulated trajectories. Incorporating into SINDy the assumption that each falling object is governed by a similar physical law is shown to improve the robustness of the learned models, but discrepancies between the predictions and observations persist due to subtleties in drag dynamics. This work highlights the fact that the naive application of ML/AI will generally be insufficient to infer universal physical laws without further modification.
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