Physics-guided transfer learning for robust biomass gasification modeling under data scarcity

Key Points

• Robustness in biomass gasification modeling improves under data scarcity, indicating better performance.
• Key evidence shows transfer learning achieves a notable accuracy increase of up to 25% compared to traditional methods.
• Modeling approach employs physics-guided transfer learning to enhance prediction accuracy in limited data scenarios.
• Findings suggest that transfer learning can significantly aid in environmental sustainability through efficient biomass utilization.