This work proposes a Bayesian inference method for the reduced-order modeling of time-dependent systems. Informed by structure governing equations, task learning model from data is posed as inverse problem with Gaussian prior and likelihood. The resulting posterior distribution characterizes operators defining model, hence predictions subsequently issued are endowed uncertainty. statistical mom...

M ethodologies such as Extreme Programming (XP), Scrum, and agile software development promise increased customer satisfaction, lower defect rates, faster development times, and a solution to rapidly changing requirements. Plan-driven approaches such as Cleanroom, the Personal Software Process, or methods based on the Capability Maturity Model promise predictability , stability, and high assura...

Analysis of intrinsic irreversibility and heat transfer in a buoyancy-driven changeable viscosity liquid along an incline heated wall with convective cooling taking into consideration the heated isothermal and isoflux wall is investigated. By Newton’s law of cooling, we assumed the free surface exchange heat with environment and fluid viscosity is exponentially dependent on temperature. Appropr...

A data-driven adaptive reduced order modelling approach is presented for the reconstruction of impulsively started and vortex-dominated flows. residual-based error metric first time in framework approach. The Reduced Order Modelling selects locally most accurate model on basis lowest residual produced by substituting reconstructed flow field into a finite volume discretisation Navier–Stokes equ...