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A methane telemetry system for 1653 nm DFB laser based on TDLAS‐WMS technology is developed in this article. The focus tunable lens is used as the collimating system of the telemetry device to solve the problem that the telemetry device cannot be dynamically adjusted under different detection environments. Experimental results show that the root mean square error was 7.6205 and the theoretical limit of detection (LoD) was 1.473 parts per million (ppm) while the optimal integration time reaches 30 s. The near‐infrared CH4 telemetry system is suitable for different detection environments of natural gas leakage and has good detection performance and stable and reliable operation.

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In this paper, we investigate and design multiscale simulations for stochastic multiscale PDEs. As for the space, we consider a coarse grid and a known multiscale method, the generalized multiscale finite element method (GMsFEM). In order to obtain a small dimensional representation of the solution in each coarse block, the uncertainty space needs to be partitioned (coarsened). This coarsenining collects realizations that provide similar multiscale features as outlined in GMsFEM (or other method of choice). This step is known to be computationally demanding as it requires many local solves and clustering based on them. In this work, we take a different approach and learn coarsening the uncertainty space. Our methods use deep learning techniques in identifying clusters (coarsening) in the uncertainty space. We use convolutional neural networks combined with some techniques in adversary neural networks. We define appropriate loss functions in the proposed neural networks, where the loss function is composed of several parts that includes terms related to clusters and reconstruction of basis functions. We present numerical results for channelized permeability fields in the examples of flows in porous media.