New RE-PINN method enhances uncertainty estimates in physics-informed neural networks

Researchers have developed a new method called Repulsive Ensembles for Bayesian Inference in Physics-informed Neural Networks (RE-PINN) to improve uncertainty quantification in physics-informed neural networks (PINNs). This approach adds a repulsive term to the loss function, encouraging ensemble predictions to better represent the true Bayesian posterior. Unlike standard ensembles that tend to converge to single maximum-a-posteriori solutions, RE-PINN generates more diverse and accurate uncertainty estimates, proving effective for low-dimensional problems. AI

IMPACT Enhances uncertainty quantification in neural networks for scientific applications.

RANK_REASON The cluster contains a research paper detailing a new methodology for improving uncertainty quantification in neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Bayesian Posterior Confidence Narrowing
• maximum a posteriori estimation
• Monte Carlo
• Philipp Pilar
• physics-informed neural networks
• RE-PINN
• Repulsive Ensembles for Bayesian Inference in Physics-informed Neural Networks

• paper
• other

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