Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions
- Abstract Bayesian optimization with Gaussian process surrogates is a popular approach for optimizing expensive-to-evaluate functions in terms of time, energy, or computational resources. Typically, a Gaussian process models a scalar objective derived from observed data. However, in many real-world applications, the objective is a combination of multiple outputs from physical experiments or simulations. Converting these multidimensional observations into a single scalar can lead to information loss, slowing convergence and yielding suboptimal results. To address this, we propose to use multi-output Gaussian processes to learn the full vector of observations directly, before mapping them to the scalar objective via an inexpensive analytical function. This physics-informed approach retains more information from the underlying physical processes, improving surrogate model accuracy. As a result, the approach accelerates optimization and produces better final designs compared to standard implementations.
| Author: | Ivan SekulicORCiD, Jonas Schaible, Gabriel MüllerORCiD, Matthias Plock, Sven Burger, Víctor José Martínez-LahuertaORCiD, Naceur GaaloulORCiD, Philipp-Immanuel SchneiderORCiD |
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| Document Type: | Article |
| Parent Title (English): | Mach. Learn. Sci. Technol. |
| Volume: | 6 |
| First Page: | 040503 |
| Publisher: | IOP Publishing |
| Year of first publication: | 2025 |
| DOI: | https://doi.org/10.1088/2632-2153/ae1f5f |

