Researchers from the Centre for Advanced Laser Applications have developed a novel approach that combines multi-objective and multi-fidelity optimization, which could significantly speed up the optimization of complex systems like laser-plasma accelerators. The work, recently published in IOP Machine Learning: Science and Technology, introduces the innovative use of a trust metric to facilitate the joint optimization of multiple objectives and data sources.

The new "Trust-based Multi-Objective Multi-Fidelity" (Trust-MOMF) optimization method modifies the standard multi-objective Bayesian optimization approach by incorporating the trust gain per evaluation cost as an additional objective. This allows the algorithm to simultaneously optimize multiple objectives while leveraging low-fidelity, computationally cheap approximations to efficiently establish the Pareto set of optimal solutions.

When applied to particle-in-cell simulations of laser-plasma acceleration, a highly complex and computationally expensive problem, the Trust-MOMF approach yielded optimization results in a fraction of the time compared to standard methods. "Our method can reduce the computational cost of multi-objective optimization by an order of magnitude," said Dr. Andreas Döpp, who lead the study. "This will enable us to explore a much wider parameter space in simulations and experiments."

By providing an efficient way to optimize systems with multiple, potentially competing objectives, the Trust-MOMF method paves the way for accelerating both computational and experimental research, not just in laser-plasma physics but across various scientific computing domains. The team is already applying this approach to optimize laser-plasma acceleration experiments at the ATLAS-3000 laser facility.

Original publication:

Leveraging trust for joint multi-objective and multifidelity optimization

F. Irshad, S. Karsch, A. Döpp

Machine Learning: Science and Technology 5, 015056 (2024)

News from: https://cala-laser.de/news/article/new-method-enables-efficient-optimization-of-complex-systems.html

Explore further:

1. Control systems and data management for high-power laser facilities

2. Data-driven science and machine learning methods in laser–plasma physics

3. Tango Controls and data pipeline for petawatt laser experiments

4. Hyperspectral compressive wavefront sensing

5. Applications of object detection networks in high-power laser systems and experiments