Physics-informed shape optimization using coordinate projection.

ABSTRACT: The rapid growth of artificial intelligence is revolutionizing classical engineering society, offering novel approaches to material and structural design and analysis. Among various scientific machine learning techniques, physics-informed neural network (PINN) has been one of the most researched subjects, for its ability to incorporate physics prior knowledge into model training. However, the intrinsic continuity requirement of PINN demands the adoption of domain decomposition when multiple materials with distinct properties exist. This greatly complicates the gradient computation of design features, restricting the application of PINN to structural shape optimization. To address this, we present a novel framework that employs neural network coordinate projection for shape optimization wit

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC10951326 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature