feMo connects finite-element models to gradient-based multidisciplinary optimization. The associated framework integrates FEniCSx with CSDL, combining derivatives of PDE residuals with sensitivities from other disciplines. Demonstrations include electric-motor shape optimization and aeroelastic analysis, illustrating how field-based physics can participate in a larger system model [1].
References
- Ru Xiang, Sebastiaan P. C. van Schie, Luca Scotzniovsky, Jiayao Yan, David Kamensky, John T. Hwang. Automating adjoint sensitivity analysis for multidisciplinary models involving partial differential equations. Structural and Multidisciplinary Optimization (in press 2024, per CV), 2024.DOIPDF
@article{xiang2024automating, author = {Ru Xiang and Sebastiaan P. C. van Schie and Luca Scotzniovsky and Jiayao Yan and David Kamensky and John T. Hwang}, title = {Automating adjoint sensitivity analysis for multidisciplinary models involving partial differential equations}, journal = {Structural and Multidisciplinary Optimization (in press 2024, per CV)}, year = {2024}, doi = {10.21203/rs.3.rs-4265983/v1}, url = {https://doi.org/10.21203/rs.3.rs-4265983/v1}, note = {Metadata verification: Crossref title} } - Luca Scotzniovsky, Ru Xiang, Zeyu Cheng, Gabriel Rodriguez, David Kamensky, Chris Mi, John T. Hwang. Geometric design of electric motors using adjoint-based shape optimization. Optimization and Engineering, 2025.DOIPDF
@article{scotzniovsky2025geometric, author = {Luca Scotzniovsky and Ru Xiang and Zeyu Cheng and Gabriel Rodriguez and David Kamensky and Chris Mi and John T. Hwang}, title = {Geometric design of electric motors using adjoint-based shape optimization}, journal = {Optimization and Engineering}, year = {2025}, volume = {26}, number = {1}, pages = {121-158}, doi = {10.1007/s11081-024-09892-6}, url = {https://doi.org/10.1007/s11081-024-09892-6}, note = {Metadata verification: Crossref DOI} }
Research connections