Uncertainties in wildfire simulations pose a major challenge for making decisions about fire management, mitigation, and evacuations. However, ensemble calculations to quantify uncertainties are prohibitively expensive with high-fidelity models that are needed to capture today's ever-more intense and severe wildfires. This work shows that surrogate models trained on related data enable scaling multifidelity uncertainty quantification to high-fidelity wildfire simulations of unprecedented scale with billions of degrees of freedom.
View Article and Find Full Text PDFThe objective was to investigate clinical and radiological outcomes of rehabilitations with root-analogue implants (RAIs). Patients restored with RAIs, supporting single crowns or fixed dental prostheses, were recruited for follow-up examinations. Besides clinical and esthetical evaluations, X-rays were taken and compared with the records.
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