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The Chaos Machine Pdf

In mathematics, a chaos machine is a class of algorithms constructed on the base of chaos theory (mainly deterministic chaos) to produce pseudo-random oracle. It represents the idea of creating a universal scheme with modular design and customizable parameters, which can be applied wherever randomness and sensitiveness is needed.[1]

Abstract. The heterogeneous chemistry of atmospheric aerosols involves multiphase chemical kinetics that can be described by kinetic multi-layer models (KM) explicitly resolving mass transport and chemical reaction. However, KM are computationally too expensive to be used as sub-modules in large-scale atmospheric models, and the computational costs also limit their utility in inverse modelling approaches commonly used to infer aerosol kinetic parameters from laboratory studies. In this study, we show how machine learning methods can generate inexpensive surrogate models based on the kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB). We apply and compare two common and openly available methods for the generation of surrogate models, polynomial chaos expansion (PCE) with UQLab and neural networks (NN) through the Python package Keras. We show that the PCE method is well-suited to determine global sensitivity indices of the KM and demonstrate how inverse modelling applications can be enabled or accelerated with NN-suggested sampling. These qualities make them suitable supporting tools for laboratory work in the interpretation of data and design of future experiments. Overall, the KM surrogate models investigated in this study are fast, accurate, and robust, which suggests their applicability as sub-modules in large-scale atmospheric models.

Berkemeier et al. present two surrogate model approaches, polynomial chaos expansion and neural networks, used to predict discrete reaction times of kinetic multi-layer models and used for sensitivity analysis and inverse modelling. The paper is well-organized, concise, and appropriate for GMD. I recommend its publication after addressing the comments below.

One general comment: It is not clear in a first read what the surrogate models are predicting (in other words, what the targets are). On a second read, it seems like the targets are the order of magnitudes of 3 different reaction times: the 90% reaction time, half-life, and 10% reaction time. Using these targets instead of concentration targets makes sense if the application is machine learning aided sampling rather than full model replacement. However, many readers (including myself) might initially assume that surrogate models replicate the output of their reference model, so this non-standard target should be addressed earlier on.

Berkemeier et al. present work using neural networks and polynomial chaos expansion to emulate complex models of multiphase kinetics for atmospheric aerosols. They find that both techniques are suitable for emulation, and assess the benefits and drawbacks of each approach. In general, the work is well-presented, addresses an important topic, and provides a valuable contribution to the scientific literature. I have a few comments that I believe should be addressed before this paper is accepted for publication.

Maybe our understanding of such an oxymoronic relationship with social media platforms would make us seek alternative human-centered paths to move the AI needle from the chaos of the techno-centric toxicity to active social engagement. 59ce067264

https://www.soulsearchcoaching.ch/group/mysite-231-group/discussion/9008cd70-8103-46fa-9fe9-a45b17d2de48

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