Show simple item record

dc.contributor.authorSun, Yubiao
dc.contributor.authorSun, Qiankun
dc.contributor.authorQin, Kan
dc.date.accessioned2021-11-22T14:49:17Z
dc.date.available2021-11-22T14:49:17Z
dc.date.issued2021-11-19
dc.identifier.issn1996-1073
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330928
dc.description.abstract<jats:p>It is the tradition for the fluid community to study fluid dynamics problems via numerical simulations such as finite-element, finite-difference and finite-volume methods. These approaches use various mesh techniques to discretize a complicated geometry and eventually convert governing equations into finite-dimensional algebraic systems. To date, many attempts have been made by exploiting machine learning to solve flow problems. However, conventional data-driven machine learning algorithms require heavy inputs of large labeled data, which is computationally expensive for complex and multi-physics problems. In this paper, we proposed a data-free, physics-driven deep learning approach to solve various low-speed flow problems and demonstrated its robustness in generating reliable solutions. Instead of feeding neural networks large labeled data, we exploited the known physical laws and incorporated this physics into a neural network to relax the strict requirement of big data and improve prediction accuracy. The employed physics-informed neural networks (PINNs) provide a feasible and cheap alternative to approximate the solution of differential equations with specified initial and boundary conditions. Approximate solutions of physical equations can be obtained via the minimization of the customized objective function, which consists of residuals satisfying differential operators, the initial/boundary conditions as well as the mean-squared errors between predictions and target values. This new approach is data efficient and can greatly lower the computational cost for large and complex geometries. The capacity and generality of the proposed method have been assessed by solving various flow and transport problems, including the flow past cylinder, linear Poisson, heat conduction and the Taylor–Green vortex problem.</jats:p>
dc.languageen
dc.publisherMDPI AG
dc.subjectdeep learning
dc.subjectphysics-informed neural networks
dc.subjectpartial differential equation
dc.subjectautomatic differentiation
dc.subjectsurrogate model
dc.titlePhysics-Based Deep Learning for Flow Problems
dc.typeArticle
dc.date.updated2021-11-22T14:49:16Z
prism.issueIdentifier22
prism.publicationNameEnergies
prism.volume14
dc.identifier.doi10.17863/CAM.78371
dcterms.dateAccepted2021-11-16
rioxxterms.versionofrecord10.3390/en14227760
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn1996-1073
cam.issuedOnline2021-11-19


Files in this item

Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record