Multi-dimensional parameter spaces are commonly encountered in astroparticle
physics theories that attempt to capture novel phenomena. However, they often
possess complicated posterior geometries that are expensive to traverse using
techniques traditional to this community. Effectively sampling these spaces is
crucial to bridge the gap between experiment and theory. Several recent
innovations, which are only beginning to make their way into this field, have
made navigating such complex posteriors possible. These include GPU
acceleration, automatic differentiation, and neural-network-guided
reparameterization. We apply these advancements to astroparticle physics
experimental results in the context of novel neutrino physics and benchmark
their performances against traditional nested sampling techniques. Compared to
nested sampling alone, we find that these techniques increase performance for
both nested sampling and Hamiltonian Monte Carlo, accelerating inference by
factors of $\sim 100$ and $\sim 60$, respectively. As nested sampling also
evaluates the Bayesian evidence, these advancements can be exploited to improve
model comparison performance while retaining compatibility with existing
implementations that are widely used in the natural sciences.

应用 GPU 加速、自动差分和神经网络引导重新参数化等新技术，我们研究了多维参数空间中的天体粒子物理实验结果，并将其性能与传统嵌套采样技术进行了对比，发现这些技术可以加速推理过程，提高模型比较性能。

利用自动微分和神经传输在天体粒子物理中进行快速推断

Fast Inference Using Automatic Differentiation and Neural Transport in  Astroparticle Physics

Both computational and experimental material discovery bring forth the
challenge of exploring multidimensional and often non-differentiable parameter
spaces, such as phase diagrams of Hamiltonians with multiple interactions,
composition spaces of combinatorial libraries, processing spaces, and molecular
embedding spaces. Often these systems are expensive or time-consuming to
evaluate a single instance, and hence classical approaches based on exhaustive
grid or random search are too data intensive. This resulted in strong interest
towards active learning methods such as Bayesian optimization (BO) where the
adaptive exploration occurs based on human learning (discovery) objective.
However, classical BO is based on a predefined optimization target, and
policies balancing exploration and exploitation are purely data driven. In
practical settings, the domain expert can pose prior knowledge on the system in
form of partially known physics laws and often varies exploration policies
during the experiment. Here, we explore interactive workflows building on
multi-fidelity BO (MFBO), starting with classical (data-driven) MFBO, then
structured (physics-driven) sMFBO, and extending it to allow human in the loop
interactive iMFBO workflows for adaptive and domain expert aligned exploration.
These approaches are demonstrated over highly non-smooth multi-fidelity
simulation data generated from an Ising model, considering spin-spin
interaction as parameter space, lattice sizes as fidelity spaces, and the
objective as maximizing heat capacity. Detailed analysis and comparison show
the impact of physics knowledge injection and on-the-fly human decisions for
improved exploration, current challenges, and potential opportunities for
algorithm development with combining data, physics and real time human
decisions.

在计算材料发现领域，通过多样的交互工作流程和基于贝叶斯优化的多层次恒定调整，结合数据、物理和实时人工决策，提高了对多维参数空间的探索效率。

加速物理发现的非交互性和交互性多保真贝叶斯优化：当前挑战和未来机遇

Towards accelerating physical discovery via non-interactive and  interactive multi-fidelity Bayesian Optimization: Current challenges and  future opportunities