Barak Fishbain

Ofek Aloni, Barak Fishbain

Generating dense physical fields from sparse measurements is a fundamental question in sampling, signal processing, and many other applications. State-of-the-art methods either use spatial statistics or rely on examples of dense fields in the training phase, which often are not available, and thus rely on synthetic data. Here, we present a reconstruction method that generates dense fields from sparse measurements, without assuming availability of the spatial statistics, nor of examples of the dense fields. This is made possible through the introduction of an automatically differentiable numerical simulator into the training phase of the method. The method is shown to have superior results over statistical and neural network based methods on a set of three standard problems from fluid mechanics.

Noam Ginio, Michael Lindenbaum, Barak Fishbain et al.

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. The developed method training and evaluation was based on in-house made supervised dataset. Here we present this supervised dataset of polarimetric images of the water surface coupled with the water surface elevation measurements made by a linear array of resistance-type wave gauges (WG). The water waves were mechanically generated in a laboratory waves basin, and the polarimetric images were captured under an artificial light source. Meticulous camera and WGs calibration and instruments synchronization supported high spatio-temporal resolution. The data set covers several wavefield conditions, from simple monochromatic wave trains of various steepness, to irregular wavefield of JONSWAP prescribed spectral shape and several wave breaking scenarios. The dataset contains measurements repeated in several camera positions relative to the wave field propagation direction.

Noam Ginio, Michael Lindenbaum, Barak Fishbain et al.

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are crucial for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limitations in wavenumber/frequency response. To address these challenges, we propose Wave (from) Polarized Light Learning (WPLL), a learning based remote sensing method for laboratory implementation, capable of inferring surface elevation and slope maps in high resolution. The method uses the polarization properties of light reflected from the water surface. The WPLL uses a deep neural network (DNN) model that approximates the water surface slopes from the polarized light intensities. Once trained on simple monochromatic wave trains, the WPLL is capable of producing high-resolution and accurate 2D reconstruction of the water surface slopes and elevation in a variety of irregular wave fields. The method's robustness is demonstrated by showcasing its high wavenumber/frequency response, its ability to reconstruct wave fields propagating at arbitrary angles relative to the camera optical axis, and its computational efficiency. This developed methodology is an accurate and cost-effective near-real time remote sensing tool for laboratory water surface waves measurements, setting the path for upscaling to open sea application for research, monitoring, and short-time forecasting.