Physics-AI symbiosis

Jalali, Bahram; Zhou, Yiming; Kadambi, Achuta; Roychowdhury, Vwani P.

doi:10.1088/2632-2153/ac9215

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…In recent years, with the increasing availability and accessibility of data, data-driven approaches-and machine learning in particular-have attracted increasing attention among fluid researchers as a faster and cheaper alternative or complement to experimental and numerical studies [32][33][34][35][36][37][38][39]. Regarding drop impacts, several machine-learning-based studies have been carried out [40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the morphological evolution of a splashing drop using an encoder–decoder

Yee¹,

Igarashi²,

Miyatake³

et al. 2023

Mach. Learn.: Sci. Technol.

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The impact of a drop on a solid surface is an important phenomenon that has various implications and applications. However, the multiphase nature of this phenomenon causes complications in the prediction of its morphological evolution, especially when the drop splashes. While most machine-learning-based drop-impact studies have centred around physical parameters, this study used a computer-vision strategy by training an encoder-decoder to predict the drop morphologies using image data. Herein, we show that this trained encoder-decoder is able to successfully generate videos that show the morphologies of splashing and non-splashing drops. Remarkably, in each frame of these generated videos, the spreading diameter of the drop was found to be in good agreement with that of the actual videos. Moreover, there was also a high accuracy in splashing/non-splashing prediction. These findings demonstrate the ability of the trained encoder-decoder to generate videos that can accurately represent the drop morphologies. This approach provides a faster and cheaper alternative to experimental and numerical studies.

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Section: Introductionmentioning

confidence: 99%

Prediction of the morphological evolution of a splashing drop using an encoder–decoder

Yee¹,

Igarashi²,

Miyatake³

et al. 2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The ever-increasing complexity of the networks is powered by the advances in semiconductor technology described by the Moore's law. The projected end of the Moore's law is a concern for the future evolution of neural networks [1]. Additionally, the neural networks must be trained and this requires a massive amount of labeled data the number of which scales with the network complexity.…”

Section: Introductionmentioning

confidence: 99%

Low latency computing for time stretch instruments

Zhou,

Jalali

2023

J. Phys. Photonics

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Time stretch instruments have been exceptionally successful in discovering single-shot ultrafast phenomena such as optical rogue waves and have led to record-speed microscopy, spectroscopy, lidar, etc. These instruments encode the ultrafast events into the spectrum of a femtosecond pulse and then dilate the time scale of the data using group velocity dispersion. Generating as much as Tbit per second of data, they are ideal partners for deep learning networks which by their inherent complexity, require large datasets for training. However, the inference time scale of neural networks in the millisecond regime is orders of magnitude longer than the data acquisition rate of time stretch instruments. This underscores the need to explore means where some of the lower-level computational tasks can be done while the data is still in the optical domain. The Nonlinear Schrödinger Kernel computing addresses this predicament. It utilizes optical nonlinearities to map the data onto a new domain in which classification accuracy is enhanced, without increasing the data dimensions. One limitation of this technique is the fixed optical transfer function, which prevents training and generalizability. Here we show that the optical kernel can be effectively tuned and trained by utilizing digital phase encoding of the femtosecond laser pulse leading to a reduction of the error rate in data classification.

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Weather Forecasting Limitations in the Developing World

Lofstead

2023

Distributed, Ambient and Pervasive Interactions

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Physics-AI symbiosis

Cited by 5 publications

References 43 publications

Prediction of the morphological evolution of a splashing drop using an encoder–decoder

Prediction of the morphological evolution of a splashing drop using an encoder–decoder

Low latency computing for time stretch instruments

Weather Forecasting Limitations in the Developing World

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