FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285251510> ?p ?o ?g. }

• W4285251510 endingPage "5178" @default.
• W4285251510 startingPage "5165" @default.
• W4285251510 abstract "One fundamental difficulty in multiphysics numerical simulation is the complex interactions between different physics domains leading to plenty of computational costs. Although neural networks have recently been introduced in multiphysics simulations, the modeling complexity and the enormous amount of training data required may still pose significant challenges to researchers. In this work, we introduce a low-cost, electromagnetic-centric, multiphysics modeling approach to simulate microwave filters. With ground-truth datasets being generated from the finite element method, a novel deep hybrid neural network (DHNN) model structure is introduced, which uses the sigmoid and the ReLU functions as activators to mimic the diversity of biological neurons. A new, more feasible training algorithm is proposed for the efficient development of the DHNN model. The algorithm adopts the design-of-experiment (DOE) sampling technique and is specifically designed for the simulation of multiphysics responses. The strong approximation ability of the DHNN can lead to high-accuracy modeling with fewer training data and less resource consumption. Another advantage of this approach is that the modeling process is more concise and easier to apply compared with other modeling technologies. Numerical examples show that the DHNN can achieve higher accurate results with much less training data compared to traditional ANNs. The advantages of the proposed method in computational efficiency are more pronounced, especially when the amount of input data increases." @default.
• W4285251510 created "2022-07-14" @default.
• W4285251510 creator A5017941291 @default.
• W4285251510 creator A5043244896 @default.
• W4285251510 creator A5068736638 @default.
• W4285251510 creator A5083064427 @default.
• W4285251510 creator A5089905627 @default.
• W4285251510 date "2022-07-01" @default.
• W4285251510 modified "2023-10-16" @default.
• W4285251510 title "Fast Multi-Physics Simulation of Microwave Filters via Deep Hybrid Neural Network" @default.
• W4285251510 cites W1499091328 @default.
• W4285251510 cites W1944757151 @default.
• W4285251510 cites W2001363185 @default.
• W4285251510 cites W2006131791 @default.
• W4285251510 cites W2019057054 @default.
• W4285251510 cites W2098103715 @default.
• W4285251510 cites W2101377736 @default.
• W4285251510 cites W2102294719 @default.
• W4285251510 cites W2114250267 @default.
• W4285251510 cites W2119578017 @default.
• W4285251510 cites W2122434383 @default.
• W4285251510 cites W2139802970 @default.
• W4285251510 cites W2149430717 @default.
• W4285251510 cites W2155174148 @default.
• W4285251510 cites W2162708855 @default.
• W4285251510 cites W2162936190 @default.
• W4285251510 cites W2328866163 @default.
• W4285251510 cites W2345050209 @default.
• W4285251510 cites W2506876909 @default.
• W4285251510 cites W2789358930 @default.
• W4285251510 cites W2801564747 @default.
• W4285251510 cites W2883018178 @default.
• W4285251510 cites W2891712674 @default.
• W4285251510 cites W2921384035 @default.
• W4285251510 cites W2942883129 @default.
• W4285251510 cites W2958228360 @default.
• W4285251510 cites W2968784654 @default.
• W4285251510 cites W2979711203 @default.
• W4285251510 cites W3106708394 @default.
• W4285251510 cites W3128855577 @default.
• W4285251510 cites W3163102676 @default.
• W4285251510 cites W3175804269 @default.
• W4285251510 cites W3192382242 @default.
• W4285251510 cites W3201757956 @default.
• W4285251510 cites W3217759650 @default.
• W4285251510 cites W4210550248 @default.
• W4285251510 cites W4243889563 @default.
• W4285251510 cites W4248569332 @default.
• W4285251510 doi "https://doi.org/10.1109/tap.2022.3188627" @default.
• W4285251510 hasPublicationYear "2022" @default.
• W4285251510 type Work @default.
• W4285251510 citedByCount "6" @default.
• W4285251510 countsByYear W42852515102023 @default.
• W4285251510 crossrefType "journal-article" @default.
• W4285251510 hasAuthorship W4285251510A5017941291 @default.
• W4285251510 hasAuthorship W4285251510A5043244896 @default.
• W4285251510 hasAuthorship W4285251510A5068736638 @default.
• W4285251510 hasAuthorship W4285251510A5083064427 @default.
• W4285251510 hasAuthorship W4285251510A5089905627 @default.
• W4285251510 hasConcept C111919701 @default.
• W4285251510 hasConcept C113775141 @default.
• W4285251510 hasConcept C11413529 @default.
• W4285251510 hasConcept C119857082 @default.
• W4285251510 hasConcept C127413603 @default.
• W4285251510 hasConcept C135628077 @default.
• W4285251510 hasConcept C154945302 @default.
• W4285251510 hasConcept C41008148 @default.
• W4285251510 hasConcept C459310 @default.
• W4285251510 hasConcept C46435376 @default.
• W4285251510 hasConcept C50644808 @default.
• W4285251510 hasConcept C66938386 @default.
• W4285251510 hasConcept C98045186 @default.
• W4285251510 hasConceptScore W4285251510C111919701 @default.
• W4285251510 hasConceptScore W4285251510C113775141 @default.
• W4285251510 hasConceptScore W4285251510C11413529 @default.
• W4285251510 hasConceptScore W4285251510C119857082 @default.
• W4285251510 hasConceptScore W4285251510C127413603 @default.
• W4285251510 hasConceptScore W4285251510C135628077 @default.
• W4285251510 hasConceptScore W4285251510C154945302 @default.
• W4285251510 hasConceptScore W4285251510C41008148 @default.
• W4285251510 hasConceptScore W4285251510C459310 @default.
• W4285251510 hasConceptScore W4285251510C46435376 @default.
• W4285251510 hasConceptScore W4285251510C50644808 @default.
• W4285251510 hasConceptScore W4285251510C66938386 @default.
• W4285251510 hasConceptScore W4285251510C98045186 @default.
• W4285251510 hasFunder F4320321001 @default.
• W4285251510 hasFunder F4320336567 @default.
• W4285251510 hasIssue "7" @default.
• W4285251510 hasLocation W42852515101 @default.
• W4285251510 hasOpenAccess W4285251510 @default.
• W4285251510 hasPrimaryLocation W42852515101 @default.
• W4285251510 hasRelatedWork W1552428517 @default.
• W4285251510 hasRelatedWork W2001551372 @default.
• W4285251510 hasRelatedWork W2010566957 @default.
• W4285251510 hasRelatedWork W2080525111 @default.
• W4285251510 hasRelatedWork W2139580322 @default.
• W4285251510 hasRelatedWork W274022721 @default.
• W4285251510 hasRelatedWork W2961085424 @default.

• next