Physics Enhanced Artificial Intelligence
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Abstract (arXiv)
We propose that intelligently combining models from the domains of Artificial Intelligence or Machine Learning with Physical and Expert models will yield a more "trustworthy" model than any one model from a single domain, given a complex and narrow enough problem. Based on mean-variance portfolio theory and bias-variance trade-off analysis, we prove combining models from various domains produces a model that has lower risk, increasing user trust. We call such combined models - physics enhanced artificial intelligence (PEAI), and suggest use cases for PEAI.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Mar 2019.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.
- 2023Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applicationscited by 6
- 2024Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networkscited by 4
- 2025A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environmentscited by 2
- 2025A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environments14 Jul 2025
- 2024Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks2 Oct 2024
- 2023Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications31 Oct 2023