Improving Wheatstone bridge sensitivity with computational simulations and bayesian optimization

Abstract The Wheatstone bridge is essential for precise resistance measurements and valuable for both undergraduate physics education and real-world scientific research. This study reimagines an experiment by integrating computational simulations with traditional methods, thereby enhancing the educational and practical value of the experiment. By analysing key factors, such as the internal resistances of the galvanometer and power supply, and optimising resistor configurations, we demonstrate pathways to maximise sensitivity. The tests illustrate that the optimized configuration achieves a 49% improvement in sensitivity compared with the conventional equal-arm Wheatstone bridge, with a further possible enhancement of approximately 30% if the tandem configuration proposed by Zhang et al [Zhang Y et al 2024 IEEE Sensors J. 24 9498–505] is adopted. A Bayesian optimisation-based software tool was developed to automate sensitivity calculations and guide optimal component selection. This approach bridges theoretical concepts and experimental applications to equip students with valuable skills in both the experimental and computational aspects of physics as well as prepare them for modern scientific challenges.