PiGGO integrates a learned graph neural ODE as the continuous-time dynamics model within an extended Kalman filter to enable online virtual sensing and uncertainty-aware state estimation for nonlinear dynamic systems with unknown model form and sparse sensing.
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CAttBiGNN is a bipartite GNN with edge-aware cross attention that predicts coupled nodal displacements and elemental thinning for autoregressive rollout of explicit dynamic FE simulations on dome and corner forming benchmarks, outperforming node-centered baselines.
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PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertainty
PiGGO integrates a learned graph neural ODE as the continuous-time dynamics model within an extended Kalman filter to enable online virtual sensing and uncertainty-aware state estimation for nonlinear dynamic systems with unknown model form and sparse sensing.
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A finite-element-inspired bipartite graph learned simulator for manufacturability assessment in large-deformation sheet forming
CAttBiGNN is a bipartite GNN with edge-aware cross attention that predicts coupled nodal displacements and elemental thinning for autoregressive rollout of explicit dynamic FE simulations on dome and corner forming benchmarks, outperforming node-centered baselines.