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<p>The paper explores the possibility of using the novel Deep Operator Networks (DeepONet) for forward analysis of numerically intensive and challenging multiphysics designs and optimizations of advanced materials and processes. As an important step towards that goal, DeepONet networks were devised and trained on GPUs to solve the Poisson equation (heat-conduction equation) with the spatially variable heat source and highly nonlinear stress distributions under plastic deformation with variable loads and material properties. Since DeepONet can learn the parametric solution of various phenomena and processes in science and engineering, it was found that a properly trained DeepONet can instantly and accurately inference thermal and mechanical solutions for new parametric inputs without re-training and transfer learning and several orders of magnitude faster than classical numerical methods.</p>
</abstract>

The paper explores the possibility of using the novel Deep Operator Networks (DeepONet) for forward analysis of numerically intensive and challenging multiphysics designs and optimizations of advanced materials and processes. As an important step towards that goal, DeepONet networks were devised and trained on GPUs to solve the Poisson equation (heat-conduction equation) with the spatially variable heat source and highly nonlinear stress distributions under plastic deformation with variable loads and material properties. Since DeepONet can learn the parametric solution of various phenomena and processes in science and engineering, it was found that a properly trained DeepONet can instantly and accurately inference thermal and mechanical solutions for new parametric inputs without re-training and transfer learning and several orders of magnitude faster than classical numerical methods.

About Applications of Deep Learning Operator Networks for Design and Optimization of Advanced Materials and Processes

B&H Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The paper explores the possibility of using the novel Deep Operator Networks (DeepONet) for forward analysis of numerically intensive and challenging...

About Applications of Deep Learning Operator Networks for Design and Optimization of Advanced Materials and Processes

Publié en ligne: 14 janv. 2023

Pages: 1 - 6

Reçu: 06 sept. 2022

Accepté: 26 sept. 2022

DOI: https://doi.org/10.2478/bhee-2022-0006

Mots clés
Machine Learning, Deep Operator Networks, Design, Optimization, Advanced Materials

© 2022 Seid Korić et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

About Applications of Deep Learning Operator Networks for Design and Optimization of Advanced Materials and Processes

Publié en ligne: 14 janv. 2023

Pages: 1 - 6

Reçu: 06 sept. 2022

Accepté: 26 sept. 2022

DOI: https://doi.org/10.2478/bhee-2022-0006

Mots clésMachine Learning, Deep Operator Networks, Design, Optimization, Advanced Materials

© 2022 Seid Korić et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Machine Learning, Deep Operator Networks, Design, Optimization, Advanced Materials