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What's Changed Hpc stabilize by @alegresor in https://github.com/QMCSoftware/QMCSoftware/pull/382 Update README.md by @zitterbewegung in https://github.com/QMCSoftware/QMCSoftware/pull/385 Geometric brownian motion by @larissensium in https://github.com/QMCSoftware/QMCSoftware/pull/392 QMCPy Overhaul by @alegresor in https://github.com/QMCSoftware/QMCSoftware/pull/391 v2.0 by @alegresor in https://github.com/QMCSoftware/QMCSoftware/pull/394 New Contributors @larissensium made their first contribution in https://github.com/QMCSoftware/QMCSoftware/pull/392 Full Changelog: https://github.com/QMCSoftware/QMCSoftware/compare/v1.6.1...v2.0 

Adapting to a priori unknown noise level is a very important but challenging problem in sequential decision-making as efficient exploration typically requires knowledge of the noise level, which is often loosely specified. We report significant progress in addressing this issue in linear bandits in two respects. First, we propose a novel confidence set that is ’semi-adaptive’ to the unknown sub-Gaussian parameter $$\sigma_*^2$$ in the sense that the (normalized) confidence width scales with $$\sqrt{d\sigma_*^2 + \sigma_0^2}$$ where $$d$$ is the dimension and $$\sigma_0^2$$ is the specified sub-Gaussian parameter (known) that can be much larger than $$\sigma_*^2$$. This is a significant improvement over $$\sqrt{d\sigma_0^2}$$ of the standard confidence set of Abbasi-Yadkori et al. (2011), especially when $$d$$ is large. We show that this leads to an improved regret bound in linear bandits. Second, for bounded rewards, we propose a novel variance-adaptive confidence set that has a much improved numerical performance upon prior art. We then apply this confidence set to develop, as we claim, the first practical variance-adaptive linear bandit algorithm via an optimistic approach, which is enabled by our novel regret analysis technique. Both of our confidence sets rely critically on ‘regret equality’ from online learning. Our empirical evaluation in Bayesian optimization tasks shows that our algorithms demonstrate better or comparable performance compared to existing methods. 

Nanophotonic structures have versatile applications including solar cells, antireflective coatings, electromagnetic interference shielding, optical filters, and light emitting diodes. To design and understand these nanophotonic structures, electrodynamic simulations are essential. These simulations enable us to model electromagnetic fields over time and calculate optical properties. In this work, we introduce frameworks and benchmarks to evaluate nanophotonic structures in the context of parametric structure design problems. The benchmarks are instrumental in assessing the performance of optimization algorithms and identifying an optimal structure based on target optical properties. Moreover, we explore the impact of varying grid sizes in electrodynamic simulations, shedding light on how evaluation fidelity can be strategically leveraged in enhancing structure designs. 

The design of optical devices is a complex and time-consuming process. To simplify this process, we present a novel framework of multi-fidelity multi-objective Bayesian optimization with warm starts, called Multi-BOWS. This approach automatically discovers new nanophotonic structures by managing multiple competing objectives and utilizing multi-fidelity evaluations during the design process. We employ our Multi-BOWS method to design an optical device specifically for transparent electromagnetic shielding, a challenge that demands balancing visible light transparency and effective protection against electromagnetic waves. Our approach leverages the understanding that simulations with a coarser mesh grid are faster, albeit less accurate than those using a denser mesh grid. Unlike the earlier multi-fidelity multi-objective method, Multi-BOWS begins with faster, less accurate evaluations, which we refer to as “warm-starting,” before shifting to a dense mesh grid to increase accuracy. As a result, Multi-BOWS demonstrates 3.2–89.9% larger normalized area under the Pareto frontier, which measures a balance between transparency and shielding effectiveness, than low-fidelity only and high-fidelity only techniques for the nanophotonic structures studied in this work. Moreover, our method outperforms an existing multi-fidelity method by obtaining 0.5–10.3% larger normalized area under the Pareto frontier for the structures of interest.