APA (7th ed.) Citation

McCallum, S. G., Lerpinière, J. E., Jensen, K. O., Friederich, P., & Walker, A. B. (2023). Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations. APL Machine Learning, 1(4), 1. https://doi.org/10.1063/5.0151747

Chicago Style (17th ed.) Citation

McCallum, Samuel G., James E. Lerpinière, Kjeld O. Jensen, Pascal Friederich, and Alison B. Walker. "Bayesian Optimization Approach to Quantify the Effect of Input Parameter Uncertainty on Predictions of Numerical Physics Simulations." APL Machine Learning 1, no. 4 (2023): 1. https://doi.org/10.1063/5.0151747.

MLA (9th ed.) Citation

McCallum, Samuel G., et al. "Bayesian Optimization Approach to Quantify the Effect of Input Parameter Uncertainty on Predictions of Numerical Physics Simulations." APL Machine Learning, vol. 1, no. 4, 2023, p. 1, https://doi.org/10.1063/5.0151747.