FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3203096741> ?p ?o ?g. }

• W3203096741 abstract "The Poisson equation is critical to get a self-consistent solution in plasma fluid simulations used for Hall effect thrusters and streamer discharges, since the Poisson solution appears as a source term of the unsteady nonlinear flow equations. As a first step, solving the 2D Poisson equation with zero Dirichlet boundary conditions using a deep neural network is investigated using multiple-scale architectures, defined in terms of number of branches, depth and receptive field. One key objective is to better understand how neural networks learn the Poisson solutions and provide guidelines to achieve optimal network configurations, especially when coupled to the time-varying Euler equations with plasma source terms. Here, the Receptive Field is found critical to correctly capture large topological structures of the field. The investigation of multiple architectures, losses, and hyperparameters provides an optimal network to solve accurately the steady Poisson problem. The performance of the optimal neural network solver, called PlasmaNet, is then monitored on meshes with increasing number of nodes, and compared with classical parallel linear solvers. Next, PlasmaNet is coupled with an unsteady Euler plasma fluid equations solver in the context of the electron plasma oscillation test case. In this time-evolving problem, a physical loss is necessary to produce a stable simulation. PlasmaNet is finally tested on a more complex case of discharge propagation involving chemistry and advection. The guidelines established in previous sections are applied to build the CNN to solve the same Poisson equation in cylindrical coordinates with different boundary conditions. Results reveal good CNN predictions and pave the way to new computational strategies using modern GPU-based hardware to predict unsteady problems involving a Poisson equation." @default.
• W3203096741 created "2021-10-11" @default.
• W3203096741 creator A5001254676 @default.
• W3203096741 creator A5034013293 @default.
• W3203096741 creator A5042702709 @default.
• W3203096741 creator A5050489450 @default.
• W3203096741 creator A5071306154 @default.
• W3203096741 date "2021-09-27" @default.
• W3203096741 modified "2023-09-27" @default.
• W3203096741 title "Using neural networks to solve the 2D Poisson equation for electric field computation in plasma fluid simulations" @default.
• W3203096741 cites W1525132831 @default.
• W3203096741 cites W1554663460 @default.
• W3203096741 cites W1629984533 @default.
• W3203096741 cites W1871516422 @default.
• W3203096741 cites W1901129140 @default.
• W3203096741 cites W1970454553 @default.
• W3203096741 cites W2027848768 @default.
• W3203096741 cites W2040870580 @default.
• W3203096741 cites W2051917325 @default.
• W3203096741 cites W2054360187 @default.
• W3203096741 cites W206042606 @default.
• W3203096741 cites W2151162785 @default.
• W3203096741 cites W2245493112 @default.
• W3203096741 cites W2356652130 @default.
• W3203096741 cites W2501995758 @default.
• W3203096741 cites W2551520961 @default.
• W3203096741 cites W2556967412 @default.
• W3203096741 cites W2772097715 @default.
• W3203096741 cites W2784570262 @default.
• W3203096741 cites W2895786603 @default.
• W3203096741 cites W2902480423 @default.
• W3203096741 cites W2962727772 @default.
• W3203096741 cites W2963125871 @default.
• W3203096741 cites W2985630280 @default.
• W3203096741 cites W3015625225 @default.
• W3203096741 cites W3034445143 @default.
• W3203096741 cites W3034989401 @default.
• W3203096741 cites W3038186741 @default.
• W3203096741 cites W3039083618 @default.
• W3203096741 cites W3099878876 @default.
• W3203096741 cites W3102140816 @default.
• W3203096741 cites W3118310857 @default.
• W3203096741 cites W3165899507 @default.
• W3203096741 cites W3173855072 @default.
• W3203096741 cites W755414049 @default.
• W3203096741 doi "https://doi.org/10.48550/arxiv.2109.13076" @default.
• W3203096741 hasPublicationYear "2021" @default.
• W3203096741 type Work @default.
• W3203096741 sameAs 3203096741 @default.
• W3203096741 citedByCount "0" @default.
• W3203096741 crossrefType "posted-content" @default.
• W3203096741 hasAuthorship W3203096741A5001254676 @default.
• W3203096741 hasAuthorship W3203096741A5034013293 @default.
• W3203096741 hasAuthorship W3203096741A5042702709 @default.
• W3203096741 hasAuthorship W3203096741A5050489450 @default.
• W3203096741 hasAuthorship W3203096741A5071306154 @default.
• W3203096741 hasBestOaLocation W32030967411 @default.
• W3203096741 hasConcept C121332964 @default.
• W3203096741 hasConcept C126255220 @default.
• W3203096741 hasConcept C134306372 @default.
• W3203096741 hasConcept C151730666 @default.
• W3203096741 hasConcept C154945302 @default.
• W3203096741 hasConcept C158622935 @default.
• W3203096741 hasConcept C182310444 @default.
• W3203096741 hasConcept C2778770139 @default.
• W3203096741 hasConcept C2779343474 @default.
• W3203096741 hasConcept C28826006 @default.
• W3203096741 hasConcept C33923547 @default.
• W3203096741 hasConcept C38409319 @default.
• W3203096741 hasConcept C41008148 @default.
• W3203096741 hasConcept C50644808 @default.
• W3203096741 hasConcept C62520636 @default.
• W3203096741 hasConcept C62884695 @default.
• W3203096741 hasConcept C86803240 @default.
• W3203096741 hasConcept C96716743 @default.
• W3203096741 hasConceptScore W3203096741C121332964 @default.
• W3203096741 hasConceptScore W3203096741C126255220 @default.
• W3203096741 hasConceptScore W3203096741C134306372 @default.
• W3203096741 hasConceptScore W3203096741C151730666 @default.
• W3203096741 hasConceptScore W3203096741C154945302 @default.
• W3203096741 hasConceptScore W3203096741C158622935 @default.
• W3203096741 hasConceptScore W3203096741C182310444 @default.
• W3203096741 hasConceptScore W3203096741C2778770139 @default.
• W3203096741 hasConceptScore W3203096741C2779343474 @default.
• W3203096741 hasConceptScore W3203096741C28826006 @default.
• W3203096741 hasConceptScore W3203096741C33923547 @default.
• W3203096741 hasConceptScore W3203096741C38409319 @default.
• W3203096741 hasConceptScore W3203096741C41008148 @default.
• W3203096741 hasConceptScore W3203096741C50644808 @default.
• W3203096741 hasConceptScore W3203096741C62520636 @default.
• W3203096741 hasConceptScore W3203096741C62884695 @default.
• W3203096741 hasConceptScore W3203096741C86803240 @default.
• W3203096741 hasConceptScore W3203096741C96716743 @default.
• W3203096741 hasLocation W32030967411 @default.
• W3203096741 hasOpenAccess W3203096741 @default.
• W3203096741 hasPrimaryLocation W32030967411 @default.
• W3203096741 hasRelatedWork W1494666726 @default.
• W3203096741 hasRelatedWork W1577037560 @default.
• W3203096741 hasRelatedWork W1663225052 @default.
• W3203096741 hasRelatedWork W1964591033 @default.

• next