Hybrid Physics-ML Model for Forward Osmosis Flux with Complete Uncertainty Quantification

The article describes a research paper on a hybrid model combining physics and machine learning to predict forward osmosis flux. The focus on uncertainty quantification suggests a rigorous approach to model validation and reliability. The use of 'hybrid' indicates an attempt to leverage the strengths of both physics-based modeling (for understanding underlying principles) and machine learning (for data-driven prediction and potentially handling complex phenomena). The source being ArXiv suggests this is a pre-print, indicating ongoing research.

• •The research focuses on forward osmosis flux prediction.
• •The model is a hybrid of physics-based and machine learning approaches.
• •The study emphasizes complete uncertainty quantification.
• •The paper is available as a pre-print on ArXiv.