Deep generative smoke simulator: connecting simulated and real data

Wen, Jinghuan; Ma, Huimin; Luo, Xiong

doi:10.1007/s00371-019-01738-y

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Although GluGAN by Zhu et al comes closest to the proposed work in terms of the desired aim, its scope is more relevant to our prior works mentioned above 49 . However, the idea of using deep generative models for modelling in simulation environments has been exploited in other fields, such as astronomy 50 , particle physics 51 , spectral analysis 52 , protein folding 53 , and smoke sequence simulators 54 .…”

Section: Introductionmentioning

confidence: 99%

Generative deep learning for the development of a type 1 diabetes simulator

Mujahid,

Contreras,

Beneyto

et al. 2024

Commun Med

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Background Type 1 diabetes (T1D) simulators, crucial for advancing diabetes treatments, often fall short of capturing the entire complexity of the glucose-insulin system due to the imprecise approximation of the physiological models. This study introduces a simulation approach employing a conditional deep generative model. The aim is to overcome the limitations of existing T1D simulators by synthesizing virtual patients that more accurately represent the entire glucose-insulin system physiology. Methods Our methodology utilizes a sequence-to-sequence generative adversarial network to simulate virtual T1D patients causally. Causality is embedded in the model by introducing shifted input-output pairs during training, with a 90-min shift capturing the impact of input insulin and carbohydrates on blood glucose. To validate our approach, we train and evaluate the model using three distinct datasets, each consisting of 27, 12, and 10 T1D patients, respectively. In addition, we subject the trained model to further validation for closed-loop therapy, employing a state-of-the-art controller. Results The generated patients display statistical similarity to real patients when evaluated on the time-in-range results for each of the standard blood glucose ranges in T1D management along with means and variability outcomes. When tested for causality, authentic causal links are identified between the insulin, carbohydrates, and blood glucose levels of the virtual patients. The trained generative model demonstrates behaviours that are closer to reality compared to conventional T1D simulators when subjected to closed-loop insulin therapy using a state-of-the-art controller. Conclusions These results highlight our approach’s capability to accurately capture physiological dynamics and establish genuine causal relationships, holding promise for enhancing the development and evaluation of therapies in diabetes.

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Section: Introductionmentioning

confidence: 99%