Search

Artificial neural networks (ANNs) have evolved from the 1940s primitive models of brain function to become tools for artificial intelligence. They comprise many units, artificial neurons, interlinked through weighted connections. ANNs are trained to perform tasks through learning rules that modify the connection weights. With these rules being in the focus of research, ANNs have become a branch of machine learning developing independently from neuroscience. Although likely required for the development of truly intelligent machines, the integration of neuroscience into ANNs has remained a neglected proposition. Here, we demonstrate that designing an ANN along biological principles results in drastically improved task performance. As a challenging real-world problem, we choose real-time ocean-wave prediction which is essential for various maritime operations. Motivated by the similarity of ocean waves measured at a single location to sound waves arriving at the eardrum, we redesign an echo state network to resemble the brain's auditory system. This yields a powerful predictive tool which is computationally lean, robust with respect to network parameters, and works efficiently across a wide range of sea states. Our results demonstrate the advantages of integrating neuroscience with machine learning and offer a tool for use in the production of green energy from ocean waves., Comment: 23 pages, 6 figures

Infra-gravity waves are generally known as small-amplitude waves of periods between 25 seconds and 5 minutes. They originate from the presence of wave groups in the open ocean waves and can move freely after being released near the surf zone where they can be further fueled with energy from the spatially varying break point of swell waves . As these waves approach the shore, the relative importance of the infra-gravity wave signal increases, and its impact on the shorter waves gets stronger. In addition, infra-gravity waves drive strong cross-shore currents, which lead to significant back-and-forth motion of the underlying sea water. This strong cross-shore motion has been made visible by recent field studies, where significant cross-shore movement was detected and found to be correlated with the infra-gravity wave signal. In the present work, the connection between infra-gravity waves is explored further using linear wave theory and an established numerical nearshore wave model (BOSZ). It is shown that in all cases, the presence of infra-gravity waves leads to strong cross-shore motion. This behavior can be understood by considering the infra-gravity waves as separate free waves, and then following the fluid particle trajectories excited by these waves. As it is shown, these trajectories have a very large horizontal extent which -- if not separated from the main gravity wave field -- appears as a large, but often not directly visible back-and-forth motion, underlying the more readily observable gravity ocean waves., Comment: 13 pages, 5 figures

Using data from a recent field campaign, we evaluate several breaking criteria with the goal of assessing the accuracy of these criteria in wave breaking detection. Two new criteria are also evaluated. An integral parameter is defined in terms of temporal wave trough area, and a differential parameter is defined in terms of maximum steepness of the crest front period. The criteria tested here are based solely on sea surface elevation derived from standard pressure gauge records. They identify breaking and non-breaking waves with an accuracy between 84% and 89% based on the examined field data., Comment: Eight figures, two tables, submitted to Physics of Fluids

To accurately predict the consequences of nearshore waves, coastal engineers often employ numerical models. A variety of these models, broadly classified as either phase-resolving or phase-averaged, exist; each with strengths and limitations owing to the physical schematization of processes within them. Models which resolve the vertical flow structure or the full wave spectrum (i.e. sea-swell (SS) and infragravity (IG) waves) are considered more accurate, but also more computationally demanding than those with approximations. Here, we assess the speed-accuracy trade-off of six well-known wave models for overtopping (q), under shallow foreshore conditions. The results demonstrate that: i) q is underestimated by an order of magnitude when IG waves are neglected; ii) using more computationally-demanding models does not guarantee more accurate results; and iii) with empirical corrections to account for IG waves, phase-averaged models like SWAN can perform on par, if not better than, phase-resolving models but with far less computational effort.

Storm surges cause coastal inundations due to the setup of the water surface resulting from atmospheric pressure, surface winds and breaking waves. The latter is particularly difficult to be accounted for. For instance, it was observed that during Typhoon Haiyan (2013, Philippines), a stretch of coral reef near the coast, which was expected to protect the coastal communities, actually amplified the waves. The propagation and breaking process of such large nearshore waves can be successfully captured by a phase-resolving wave model. Building defences requires estimating not only the maximum storm surge height, but also maxima of breaking wave height and run-up on land, under consideration of a variety of storm characteristics. However, the computational complexity of the simulator makes optimization tasks impractical. To find the maximum breaking wave (bore) height and the maximum run-up, we employ optim-MICE, a new surrogate-based optimization scheme based on Gaussian Process emulation and information theory. In two idealised settings, we efficiently identify the conditions that create the largest storm waves at the coast using a minimal number of simulations. This is the first surrogate-based optimization of storm waves. It opens the door to previously inconceivable coastal risk assessments., Comment: arXiv admin note: text overlap with arXiv:1909.04600