The engineer adjusted the interpoles to improve commutation and reduce sparking in the generator.

The SMT solver produced several interpolants to explain why the two constraints were inconsistent.

The interpolant lies midway between the two measured points and helps reconstruct the curve.

The interpolator in the data pipeline estimated missing values based on nearby samples.

The interpolational algorithm smoothed the data between known points to produce a continuous curve.

The scientist used an interpolative algorithm to estimate missing temperature data between measurement points.

The dataset included several interpolable points that allowed the model to estimate missing values accurately.

The interpolability of the dataset determined whether we could accurately reconstruct missing values.

She adjusted the estimated path interpolatively to smooth the robot's movement between waypoints.

In numerical analysis, Hermite interpolation is often used to construct a polynomial that matches both function values and derivatives at given points.