[en] DOLFINx is the next generation problem solving environment from the FEniCS Project; it provides an expressive and performant framework for solving partial differential equations using the finite element method. Designed for parallelism from the ground up, DOLFINx supports arbitrary-degree finite elements on a wide range of (possibly curved) cell shapes across the full de Rham complex, as well as user-defined custom elements. It preserves the high level of mathematical abstraction associated with the FEniCS project, while enabling extensibility and fine-grained customization via user-defined element kernels and direct access to low-level data structures. At its core, DOLFINx adopts a modern, data-oriented and functional-inspired design and avoids the deep class hierarchies typical of more traditional finite element libraries. This approach yields a compact, maintainable, and flexible library that enables the development of high-performance programs in different languages and which enables efficient integration with other widely used numerical libraries and tools.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others Mathematics
FNR17205623 - Constraint Aware Optimization Of Topology In Design-for-additive-manufacturing, 2022 (01/11/2022-31/10/2024) - Michal Habera
Funders :
FNR - Luxembourg National Research Fund
Funding text :
The FEniCS Project is a fiscally sponsored project of NumFOCUS. MH and IB have received funding from the Google Summer of Code Program via the NumFOCUS umbrella organization.
IB and JSD acknowledge the support of EPSRC under grants EP/L015943/1 and EP/W026260/1. JPD acknowledges the support of EPSRC under grants EP/L015943/1 and EP/W026635/1. MH acknowledges the support of the Luxembourg National Research Fund under grant COAT/17205623 and the Luxembourg Ministry of Economy under the grant FEDER 2018-04-024. MWS acknowledges the support from EPSRC under grants EP/S005072/1 and EP/W007460/1. NS acknowledges the support from the NSF-EAR under grant 2021027 and the Carnegie Institution for Science with a President’s Fellowship. MER acknowledges support and funding from the Research Council of Norway via FRIPRO grant agreement #324239 (EMIx) and the U.S.–Norway Fulbright Foundation for Educational Exchange. CNR and GNW acknowledge the support of EPSRC under grants EP/P013309/1, EP/V001396/1, EP/V001345/1, EP/S005072/1, EP/W00755X/1 and EP/W026635/1.
