1 code implementation • ICML 2020 • Tianju Xue, Alex Beatson, Sigrid Adriaenssens , Ryan Adams
Optimizing the parameters of partial differential equations (PDEs), i. e., PDE-constrained optimization (PDE-CO), allows us to model natural systems from observations or perform rational design of structures with complicated mechanical, thermal, or electromagnetic properties.
1 code implementation • 15 Jun 2022 • Shuheng Liao, Tianju Xue, Jihoon Jeong, Samantha Webster, Kornel Ehmann, Jian Cao
In the numerical and experimental examples, the effectiveness of adding auxiliary training data and using the pretrained model on training efficiency and prediction accuracy, as well as the ability to identify unknown parameters with partially observed data, are demonstrated.
1 code implementation • 24 Feb 2022 • Tianju Xue, Sigrid Adriaenssens, Sheng Mao
We verify the accuracy of our machine learning approach against several representative numerical examples.
1 code implementation • NeurIPS 2021 • Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams
We compare our approach to direct optimization of the design using the learned surrogate, and to supervised learning of the synthesis problem.
no code implementations • NeurIPS 2020 • Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
We use a neural network to model the stored potential energy in a component given boundary conditions.