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Combining different existing uncertainty quantification (UQ) techniques, a framework is obtained to assess a set of metrics in computational physics problems, in general, and computational fluid dynamics (CFD), in particular. The metrics include accuracy, sensitivity and robustness of the simulator's outputs with respect to uncertain inputs and parameters. These inputs and parameters are divided into two groups: based on the variation of the first group (e.g. numerical/computational parameters such as grid resolution), a computer experiment is designed, the data of which may become uncertain due to the parameters of the second group (e.g. finite time-averaging). To construct a surrogate model based on uncertain data, Gaussian process regression (GPR) with observation-dependent (heteroscedastic) noise is used. To estimate the propagated uncertainties in the simulator's outputs from the first group of parameters, a probabilistic version of the polynomial chaos expansion (PCE) is employed Global sensitivity analysis is performed using probabilistic Sobol indices. To illustrate its capabilities, the framework is applied to the scale-resolving simulations of turbulent channel and lid-driven cavity flows using the open-source CFD solver Nek5000. It is shown that at wall distances where the time-averaging uncertainty is high, the quantities of interest are also more sensitive to numerical/computational parameters. In particular for high-fidelity codes such as Nek5000, a thorough assessment of the results' accuracy and reliability is crucial. The detailed analyses and the resulting conclusions can enhance our insight into the influence of different factors on physics simulations, in particular the simulations of high-Reynolds-number turbulent flows including wall turbulence., QC 20220627

Early and accurate neonatal sepsis detection (NSD) can help reduce mortality, morbidity and antibiotic consumption in premature infants. NSD models are often designed and evaluated in case control setups and using data derived from patient electrocardiogram (ECG) only. In this article, we evaluate our models in a more realistic retrospective cohort study setup. We use data from different modalities, including ECG, chest impedance, pulse oximetry, demographics factors and repetitive measurements of body weights. We study both the vanilla and long-short-term-memory (LSTM) Recurrent Neural Networks (RNN) architectures in a sequence to sequence mapping framework for NSD. We compare the performances of the models with logistic regression (LR) on a variety of classification metrics in a leave-one-out cross validation framework. The population we used contains 118 very low birth weight infants, among which 10 experienced sepsis. We showed that LSTM-based RNNs are both (1) more conservative and (2) more precise than LR or vanilla RNN, with a true negative rate at least +26% higher and a precision score of 0.16 compared to 0.06 for LR. This indicates that LSTM-based RNNs have the potential to reduce the false alarm rate of existing linear models, while providing a reliable diagnostic aid for neonatal sepsis., Part of ISBN 9781665470292QC 20230801

"Since the early phase of the artificial-intelligence (AI) era expectations towards AI are high, with experts believing that AI paves the way for managing and handling various global challenges. However, the significant enabling and inhibiting influence of AI for sustainable development needs to be assessed carefully, given that the technology diffuses rapidly and affects millions of people worldwide on a day-to-day basis. To address this challenge, a panel discussion was organized by the KTH Royal Institute of Technology, the AI Sustainability Center and MIT Massachusetts Institute of Technology, gathering a wide range of AI experts. This paper summarizes the insights from the panel discussion around the following themes: The role of AI in achieving the Sustainable Development Goals (SDGs) AI for a prosperous 21st century Transparency, automated decision-making processes, and personal profiling and Measuring the relevance of Digitalization and Artificial Intelligence (D&AI) at the indicator level of SDGs. The research-backed panel discussion was dedicated to recognize and prioritize the agenda for addressing the pressing research gaps for academic research, funding bodies, professionals, as well as industry with an emphasis on the transportation sector. A common conclusion across these themes was the need to go beyond the development of AI in sectorial silos, so as to understand the impacts AI might have across societal, environmental, and economic outcomes. The recordings of the panel discussion can be found at: https://www.kth.se/en/2.18487/evenemang/the-role-of-ai-in-achieving-the-sdgs-enabler-or-inhibitor-1.1001364?date=2020â 08â 20&length=1&orglength=185&orgdate=2020â 06â 30 Short link: https://bit.ly/2Kap1tE © 2021"

The massively separated wake behind a wall-mounted square cylinder is investigated by means of direct numerical simulation (DNS). The effect of inflow conditions is assessed by considering two different cases with matching momentum thickness Reynolds numbers Reθ ≃ 1000 at the location of the cylinder: one with a fully-turbulent boundary layer as inflow condition, and another one with a laminar boundary layer. The main simulation is performed by using the spectral element code Nek5000. While in the laminar-inflow simulation the horseshoe vortex forming around the cylinder can be observed in the instantaneous flow fields, this is not the case in the turbulent-inflow simulation. Besides, the streaks in the turbulent case become greatly attenuated on both sides of the obstacle. By analyzing the Reynolds shear stress uv, we show that this is due to the modulation of the horseshoe vortex by the turbulence from the incoming boundary layer. © Springer International Publishing Switzerland 2016., Correspondence Address: Vinuesa, R.; Linné FLOW Centre, KTH Mechanics, Osquars Backe 18, Sweden; email: rvinuesa@mech.kth.se. QC 20161101

This article reports on one component of a larger study on measurement of the zero-pressure-gradient turbulent flat plate boundary layer, in which a detailed investigation was conducted of the suite of corrections required for mean velocity measurements performed using Pitot tubes. In particular, the corrections for velocity shear across the tube and for blockage effects which occur when the tube is in close proximity to the wall were investigated using measurements from Pitot tubes of five different diameters, in two different facilities, and at five different Reynolds numbers ranging from Reθ = 11 100 to 67 000. Only small differences were found amongst commonly used corrections for velocity shear, but improvements were found for existing near-wall proximity corrections. Corrections for the nonlinear averaging of the velocity fluctuations were also investigated, and the results compared to hot-wire data taken as part of the same measurement campaign. The streamwise turbulence-intensity correction was found to be of comparable magnitude to that of the shear correction, and found to bring the hot-wire and Pitot results into closer agreement when applied to the data, along with the other corrections discussed and refined here., QC 20130214

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws, and principles that are invariant, robust, and causal has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventions on the system under study. With the advent of big data and data-driven methods, the fields of causal and equation discovery have developed and accelerated progress in computer science, physics, statistics, philosophy, and many applied fields. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for data-driven causal and equation discovery, point out connections, and showcase comprehensive case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is revolutionised with the efficient exploitation of observational data and simulations, modern machine learning algorithms and the combination with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.