Physics-Informed Polynomial Chaos Expansions

Surrogate modeling of costly mathematical models representing physical systems is challenging since it is typically not possible to create a large experimental design. Thus, it is beneficial to constrain the approximation to adhere to the known physics of the model. This paper presents a novel methodology for the construction of physics-informed polynomial chaos expansions (PCE) that combines the conventional experimental design with additional constraints from the physics of the model. Physical constraints investigated in this paper are represented by a set of differential equations and specified boundary conditions. A computationally efficient means for construction of physically constrained PCE is proposed and compared to standard sparse PCE. It is shown that the proposed algorithms lead to superior accuracy of the approximation and does not add significant computational burden. Although the main purpose of the proposed method lies in combining data and physical constraints, we show that physically constrained PCEs can be constructed from differential equations and boundary conditions alone without requiring evaluations of the original model. We further show that the constrained PCEs can be easily applied for uncertainty quantification through analytical post-processing of a reduced PCE filtering out the influence of all deterministic space-time variables. Several deterministic examples of increasing complexity are provided and the proposed method is applied for uncertainty quantification

Learning thermodynamically constrained equations of state with uncertainty

Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material's thermodynamic state variables -- specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in the EOS predictions. Model uncertainty is challenging for UQ studies since it requires exploring the space of all possible physically consistent functional forms. Thus, it is often neglected in favor of parametric uncertainty, which is easier to quantify without violating thermodynamic laws. This work presents a data-driven machine learning approach to constructing EOS models that naturally captures model uncertainty while satisfying the necessary thermodynamic consistency and stability constraints. We propose a novel framework based on physics-informed Gaussian process regression (GPR) that automatically captures total uncertainty in the EOS and can be jointly trained on both simulation and experimental data sources. A GPR model for the shock Hugoniot is derived and its uncertainties are quantified using the proposed framework. We apply the proposed model to learn the EOS for the diamond solid state of carbon, using both density functional theory data and experimental shock Hugoniot data to train the model and show that the prediction uncertainty reduces by considering the thermodynamic constraints.Comment: 26 pages, 7 figure

Colorimetric analysis of soil with flatbed scanners

Acknowledgements This work was supported by the Russian Science Academy Presidium (2015). The CIEDE2000 calculation of Sharma et al. (2005) was made available from spreadsheets from these authors.Peer reviewedPostprin

Contrast-Induced Nephropathy in Renal Transplant Recipients: A Single Center Experience

BACKGROUND: Contrast-induced nephropathy (CIN) in native kidneys is associated with a significant increase in mortality and morbidity. Data regarding CIN in renal allografts are limited, however. We retrospectively studied CIN in renal allografts at our institution: its incidence, risk factors, and effect on long-term outcomes including allograft loss and death. METHODS: One hundred thirty-five renal transplant recipients undergoing 161 contrast-enhanced computed tomography (CT) scans or coronary angiograms (Cath) between years 2000 and 2014 were identified. Contrast agents were iso- or low osmolar. CIN was defined as a rise in serum creatinine (SCr) by \u3e0.3 mg/dl or 25% from baseline within 4 days of contrast exposure. After excluding 85 contrast exposures where patients had no SCr within 4 days of contrast administration, 76 exposures (CT: RESULTS: Incidence of CIN was 13% following both, CT (6 out of 45) and Cath (4 out of 31). Significant bivariate predictors of CIN were IV fluid administration ( CONCLUSION: CIN is common in kidney transplant recipients, and there is room for quality improvement with regards to careful renal function monitoring post-contrast exposure. In our study

Density dependent composition of InAs quantum dots extracted from grazing incidence x-ray diffraction measurements.

Epitaxial InAs quantum dots grown on GaAs substrate are being used in several applications ranging from quantum communications to solar cells. The growth mechanism of these dots also helps us to explore fundamental aspects of self-organized processes. Here we show that composition and strain profile of the quantum dots can be tuned by controlling in-plane density of the dots over the substrate with the help of substrate-temperature profile. The compositional profile extracted from grazing incidence x-ray measurements show substantial amount of inter-diffusion of Ga and In within the QD as a function of height in the low-density region giving rise to higher variation of lattice parameters. The QDs grown with high in-plane density show much less spread in lattice parameter giving almost flat density of In over the entire height of an average QD and much narrower photoluminescence (PL) line. The results have been verified with three different amounts of In deposition giving systematic variation of the In composition as a function of average quantum dot height and average energy of PL emission.The authors would like to acknowledge the support of Department of Science and Technology (DST) for carrying out synchrotron experiments at Petra III, DESY, Germany through the DST-DESY project and the EPSRC, UK for financial support.This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/srep1573

COVID 19:Seroprevalence and vaccine responses in UK dental care professionals

Dental care professionals (DCPs) are thought to be at enhanced risk of occupational exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, robust data to support this from large-scale seroepidemiological studies are lacking. We report a longitudinal seroprevalence analysis of antibodies to SARS-CoV-2 spike glycoprotein, with baseline sampling prior to large-scale practice reopening in July 2020 and follow-up postimplementation of new public health guidance on infection prevention control (IPC) and enhanced personal protective equipment (PPE). In total, 1,507 West Midlands DCPs were recruited into this study in June 2020. Baseline seroprevalence was determined using a combined IgGAM enzyme-linked immunosorbent assay and the cohort followed longitudinally for 6 mo until January/February 2021 through the second wave of the coronavirus disease 2019 pandemic in the United Kingdom and vaccination commencement. Baseline seroprevalence was 16.3%, compared to estimates in the regional population of 6% to 7%. Seropositivity was retained in over 70% of participants at 3- and 6-mo follow-up and conferred a 75% reduced risk of infection. Nonwhite ethnicity and living in areas of greater deprivation were associated with increased baseline seroprevalence. During follow-up, no polymerase chain reaction–proven infections occurred in individuals with a baseline anti–SARS-CoV-2 IgG level greater than 147.6 IU/ml with respect to the World Health Organization international standard 20-136. After vaccination, antibody responses were more rapid and of higher magnitude in those individuals who were seropositive at baseline. Natural infection with SARS-CoV-2 prior to enhanced PPE was significantly higher in DCPs than the regional population. Natural infection leads to a serological response that remains detectable in over 70% of individuals 6 mo after initial sampling and 9 mo from the peak of the first wave of the pandemic. This response is associated with protection from future infection. Even if serological responses wane, a single dose of the Pfizer-BioNTech 162b vaccine is associated with an antibody response indicative of immunological memory

Manual / Issue 11 / Repair

Manual, a journal about art and its making. Repair. Can we find in the detail, in the stitch and the weave, an ecology of care, a model for activating new forms of life, ones that might reject or reimagine an economic and cultural order based on novelty, disposability, and the monadic self? Can they help us learn to live together in a broken world? —Brian Goldberg and Kate Irvin, from the preface to Issue 11 This volume complemented the exhibition Repair and Design Futures, on view at the RISD Museum October 5, 2018 through June 30, 2019. Softcover, 96 pages. Published 2018 by the RISD Museum. Manual 11 (Repair) contributors include Markus Berger, Gina Borromeo, Linda Catano, Thomas Denenberg, Daniel Eatock, Brian Goldberg, Ramiro Gomez, Kate Irvin, Anna Rose Keefe, Olivia Laing, Steven Lubar, Roberto Lugo, Lisa Z. Morgan, Maureen C. O’Brien, Barry Schwabsky, Sharma Shields, Jessica Urick, and Liliane Wong.https://digitalcommons.risd.edu/risdmuseum_journals/1037/thumbnail.jp

Investigation into the material properties of wooden composite structures with in-situ fibre reinforcement using additive manufacturing

In contrast to subtractive manufacturing techniques, additive manufacturing processes are known for their high efficiency in regards to utilisation of feedstock. However the various polymer, metallic and composite feedstocks used within additive manufacturing are mainly derived from energy consuming, inefficient methods, often originating from non-sustainable sources. This work explores the mechanical properties of additively manufactured composite structures fabricated from recycled sustainable wood waste with the aim of enhancing mechanical properties through glass fibre reinforcement. In the first instance, samples were formed by pouring formulation of wood waste (wood flour) and thermosetting binder (urea formaldehyde), with and without glass fibres, into a mould. The same formulations were used to additively manufacture samples via a layered deposition technique. Samples manufactured using each technique were cured and subsequently tested for their mechanical properties. Additively manufactured samples had superior mechanical properties, with up to 73% increase in tensile strength compared to moulded composites due to a densification of feedstock/paste and fibre in-situ directional alignment