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Learning Composable Energy Surrogates for PDE Order Reduction
Alex Beatson · Jordan Ash · Geoffrey Roeder · Tianju Xue · Ryan Adams

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #955

Meta-materials are an important emerging class of engineered materials in which complex macroscopic behaviour--whether electromagnetic, thermal, or mechanical--arises from modular substructure. Simulation and optimization of these materials are computationally challenging, as rich substructures necessitate high-fidelity finite element meshes to solve the governing PDEs. To address this, we leverage parametric modular structure to learn component-level surrogates, enabling cheaper high-fidelity simulation. We use a neural network to model the stored potential energy in a component given boundary conditions. This yields a structured prediction task: macroscopic behavior is determined by the minimizer of the system's total potential energy, which can be approximated by composing these surrogate models. Composable energy surrogates thus permit simulation in the reduced basis of component boundaries. Costly ground-truth simulation of the full structure is avoided, as training data are generated by performing finite element analysis of individual components. Using dataset aggregation to choose training data allows us to learn energy surrogates which produce accurate macroscopic behavior when composed, accelerating simulation of parametric meta-materials.

Author Information

Alex Beatson (Princeton University)
Jordan Ash (Microsoft Research)
Geoffrey Roeder (Princeton University X, Alphabet Inc.)
Tianju Xue (Princeton University)
Ryan Adams (Princeton University)

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