Physics-constrained machine learning for chemical engineering

Key Points

• Improved prediction accuracy results from using physics-based models combined with machine learning techniques.
• A notable increase in accuracy by 20% occurs when physics constraints guide machine learning algorithms.
• The approach combines traditional chemical engineering principles with a data-driven methodology for better modeling.
• This combination may enable more effective design strategies in chemical engineering applications.