Computer models that predict how pollutants spread through groundwater often contain errors, making forecasts unreliable. To fix this, we developed a new method that combines data-driven learning with physical laws. Our approach uses multiple physical constraints simultaneously, and dynamically adjusts their importance during calculations. This works because the extra constraints act like having additional measurements, helping the model better identify key parameters and reduce uncertainty.