Automated learning of interpretable models with quantified uncertainty

DOI10.1016/j.cma.2022.115732OpenAlexW4308599621MaRDI QIDQ2679495

J. D. Hochhalter, G. F. Bomarito, P. E. Leser, N. C. M. Strauss, K. M. Garbrecht

Publication date: 20 January 2023

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2205.01626

zbMATH Keywords

Bayesian model selectionfractional Bayes factorsymbolic regressioninterpretable machine learning

Mathematics Subject Classification ID

Biology and other natural sciences (92-XX) Computer science (68-XX)

Related Items (2)

Model-driven identification framework for optimal constitutive modeling from kinematics and rheological arrangement ⋮ Discovering stochastic partial differential equations from limited data using variational Bayes inference

Uses Software

• SymPy
• SciPy
• Python
• GitHub
• PMLB
• SMCPy
• Bingo
• SymbolicGPT

Cites Work

• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• Overall objective priors
• Sequential Monte Carlo Samplers
• An analysis of variance test for normality (complete samples)
• The case for objective Bayesian analysis
• Subjective Bayesian analysis: principles and practice
• Bayesian system ID: optimal management of parameter, model, and measurement uncertainty
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