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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators

Kislaya Ravi · Atul Agrawal · Phaedon S Koutsourelakis · Hans-Joachim Bungartz


Constrained optimization of the parameters of a simulator plays a crucial role in a design process. These problems become challenging when the simulator is stochastic, computationally expensive, and the parameter space is high-dimensional. One can efficiently perform optimization only by utilizing the gradient with respect to the parameters, but these gradients are unavailable in many legacy, black-box codes. We introduce the algorithm Scout-Nd (Stochastic Constrained Optimization for N dimensions) to tackle the issues mentioned earlier by efficiently estimating the gradient, reducing the noise of the gradient estimator, and applying multi-fidelity schemes to further reduce computational effort. We validate our approach on standard benchmarks, demonstrating its effectiveness in optimizing parameters highlighting better performance compared to existing methods.

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