Explore how Bayesian Optimization outperforms traditional Design of Experiments by finding optimal conditions with fewer experiments. Results vary between runs due to random initialization.
Watch how Bayesian Optimization selects sample points compared to space-filling Design of Experiments. The pre-rendered plot on the left shows the true optimization landscape which would be unknown in a real-world setting.
Compare sampling efficiency between Design of Experiments and Bayesian Optimization
Classic optimization benchmark with 3 global minima
Space-filling grid sampling
Measurement noise added to observations
Acquire at least 2 samples to view GP model prediction
Compare convergence rates in N-dimensional parameter spaces. As dimensionality increases, the advantage of Bayesian Optimization over traditional sampling becomes more pronounced.
Compare optimization convergence in high-dimensional spaces
Highly multi-modal with many local minima
Space-filling grid sampling
Problem dimensionality
Measurement noise
Ready to optimize your laboratory workflows?
Contact Us