Design of microwave structures and tuning parameters have mostly relied on the domain expertise of circuit designers by doing many simulations, which can be prohibitively time consuming. An inverse problem approach suggests going in the opposite direction to determine design parameters from characteristics of the desired output. In this work, we propose a novel machine learning architecture that circumvents usual design method for given quality of eye characteristics by means of a Lifelong Learning Architecture. Our proposed machine learning architecture is a large-scale coupled training system in which multiple predictions and classifications are done jointly for inverse mapping of transmission line geometry from eye characteristics. Our model is trained in a guided manner by using intra-tasks results, common Knowledge Base (KB), and coupling constraints. Our method of inverse design is general and can be applied to other applications.
Inverse Design of Transmission Lines with Deep Learning / Roy, K.; Dolatsara, M. A.; Torun, H. M.; Trinchero, R.; Swaminathan, M.. - ELETTRONICO. - (2019), pp. 1-3. (Intervento presentato al convegno 28th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019 tenutosi a McGill University Campus, can nel 2019) [10.1109/EPEPS47316.2019.193220].
Inverse Design of Transmission Lines with Deep Learning
Trinchero R.;
2019
Abstract
Design of microwave structures and tuning parameters have mostly relied on the domain expertise of circuit designers by doing many simulations, which can be prohibitively time consuming. An inverse problem approach suggests going in the opposite direction to determine design parameters from characteristics of the desired output. In this work, we propose a novel machine learning architecture that circumvents usual design method for given quality of eye characteristics by means of a Lifelong Learning Architecture. Our proposed machine learning architecture is a large-scale coupled training system in which multiple predictions and classifications are done jointly for inverse mapping of transmission line geometry from eye characteristics. Our model is trained in a guided manner by using intra-tasks results, common Knowledge Base (KB), and coupling constraints. Our method of inverse design is general and can be applied to other applications.File | Dimensione | Formato | |
---|---|---|---|
09073168.pdf
non disponibili
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
937.14 kB
Formato
Adobe PDF
|
937.14 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
EPEPS_2019_Kallol_Roy_Inverse_Design.pdf
accesso aperto
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
PUBBLICO - Tutti i diritti riservati
Dimensione
428.22 kB
Formato
Adobe PDF
|
428.22 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2836569