ADD-PINN adaptively decomposes the spatial domain based on PINN residuals and a shock indicator to improve offline traffic state estimation under the LWR model, outperforming baselines in most sparse-sensor cases while training faster.
When do extended physics- informed neural networks (XPINNs) improve generalization?,
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A graph-based neural operator trained on expert-validated race-car CFD data reaches accuracy levels usable for early-stage interactive aerodynamic design exploration.
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
citing papers explorer
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Adaptive Domain Decomposition Physics-Informed Neural Networks for Traffic State Estimation with Sparse Sensor Data
ADD-PINN adaptively decomposes the spatial domain based on PINN residuals and a shock indicator to improve offline traffic state estimation under the LWR model, outperforming baselines in most sparse-sensor cases while training faster.
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Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD
A graph-based neural operator trained on expert-validated race-car CFD data reaches accuracy levels usable for early-stage interactive aerodynamic design exploration.
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Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
- Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements