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Numerical Heat Transfer And Fluid Flow

Since their inception in the 1930–1960s, the research disciplines of computational imaging and machine learning have followed parallel tracks and, during the last two decades, experienced explosive growth drawing on similar progress in mathematical optimization and computing hardware. While these developments have always been to the benefit of image interpretation and machine vision, only recently has it become evident that machine learning architectures, and deep neural networks in particular, can be effective for computational image formation, aside from interpretation. The deep learning approach has proven to be especially attractive when the measurement is noisy and the measurement operator ill posed or uncertain. Examples reviewed here are: super-resolution; lensless retrieval of phase and complex amplitude from intensity; photon-limited scenes, including ghost imaging; and imaging through scatter. In this paper, we cast these works in a common framework. We relate the deep-learning-inspired solutions to the original computational imaging formulation and use the relationship to derive design insights, principles, and caveats of more general applicability. We also explore how the machine learning process is aided by the physics of imaging when ill posedness and uncertainties become particularly severe. It is hoped that the present unifying exposition will stimulate further progress in this promising field of research.

On the use of deep learning for computational imaging

Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the tradeoffs associated with these different reconstruction approaches, caveats and common failure modes, plus open problems and avenues for future work.

Deep Learning Techniques for Inverse Problems in Imaging

Imaging through scattering is an important yet challenging problem. Tremendous progress has been made by exploiting the deterministic input–output “transmission matrix” for a fixed medium. However, this “one-to-one” mapping is highly susceptible to speckle decorrelations – small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical “one-to-all” deep learning (DL) technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable DL approach for imaging through scattering media.

Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media

Preface 1. Wave Theory of Optical Waveguides 2. Planar Optical Waveguides 3. Optical Fibers 4. Couple Mode Theory 5. Nonlinear Optical Effects in Optical Fibers 6. Finite Element Method 7. Beam Propagation Method 8. Staircase Concatention Method 9. Planar Lightwave Circuits 10. Theorems and Formulas Appendix

Fundamentals of optical waveguides

Deep neural networks (DNNs) are used to classify and reconstruct the input images from the intensity of the speckle patterns that result after the inputs are propagated through multimode fiber (MMF). We were able to demonstrate this result for fibers up to 1 km long by training the DNNs with a database of 16,000 handwritten digits. Better recognition accuracy was obtained when the DNNs were trained to first reconstruct the input and then classify based on the recovered image. We observed remarkable robustness against environmental instabilities and tolerance to deviations of the input pattern from the patterns with which the DNN was originally trained.

Learning to see through multimode fibers

Two-phase flow boiling of R245fa, R236fa, and R1234ze(E) in 100 × 100 μm2 parallel microchannels for cooling of future 3D-ICs has been investigated. Significant flow instabilities, back flow, and non-uniform flow distribution among the channels were observed in the micro-evaporator without any inlet restrictions (micro-orifices). Therefore, to prevent such problems, rectangular restrictions were placed at the inlet of each channel and the two-phase flow flashed by the micro-orifices was identified as the most optimal operating condition. In the present study, a novel in-situ pixel-by-pixel technique was developed to calibrate the raw infra-red images, thus converting them into two-dimensional temperature fields of 10’000 pixels over the test section surface operating at 60 Hz. Tests showed that the base heat flux of 48.6 W cm−2 could be dissipated whilst keeping the micro-evaporator’s temperature below 85 °C.

Navid Borhani

Papers

Learning to see through multimode fibers

Two-phase heat transfer and high-speed visualization of refrigerant flows in 100 × 100 μm2 silicon multi-microchannels

Experimental investigation on flow boiling pressure drop and heat transfer of R1233zd(E) in a multi-microchannel evaporator

Two-phase flow operational maps for multi-microchannel evaporators

Understanding the self-sustained oscillating two-phase flow motion in a closed loop pulsating heat pipe