TW
T. Wang
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
Accuracy, Performance, and Robustness of Physics-Informed Surrogate Models
Physics-Informed Learning for CFD Surrogate Modelling and Neural Operator Methods
Machine learning-based modelling of physical systems, particularly as fast surrogate models for Computational Fluid Dynamics (CFD), has experienced increased interest in recent years. Despite some impressive progress, there is still a lack of consensus on how different neural architectures perform under physical constraints, noisy conditions, and extreme domain shifts. This paper presents a systematic comparative analysis of three popular neural surrogate models, namely a Multi-Layer Perceptron (MLP), U-Net, and Fourier Neural Operator (FNO), across time-dependent and steady-state partial differential equations, specifically the 1D Burgers' and 2D Darcy flow equations. We investigate to what extent different training strategies, including data-driven training, physics-informed regularisation, and noise augmentation, affect the accuracy, computational efficiency, and robustness of these surrogate models. Our benchmark study concludes that spatial awareness drives baseline accuracy on time-dependent flows but not uniformly across PDE types, that out-of-distribution parameter shifts reveal a universal vulnerability in complex 2D flows across all three architectures, and that physics-informed training is not a universal fix for spatially aware models. Finally, we profile computational efficiency, demonstrating that while the FNO provides predictable memory scaling, the unconstrained MLP remains unmatched for low-latency, real-time inference.
...
Machine learning-based modelling of physical systems, particularly as fast surrogate models for Computational Fluid Dynamics (CFD), has experienced increased interest in recent years. Despite some impressive progress, there is still a lack of consensus on how different neural architectures perform under physical constraints, noisy conditions, and extreme domain shifts. This paper presents a systematic comparative analysis of three popular neural surrogate models, namely a Multi-Layer Perceptron (MLP), U-Net, and Fourier Neural Operator (FNO), across time-dependent and steady-state partial differential equations, specifically the 1D Burgers' and 2D Darcy flow equations. We investigate to what extent different training strategies, including data-driven training, physics-informed regularisation, and noise augmentation, affect the accuracy, computational efficiency, and robustness of these surrogate models. Our benchmark study concludes that spatial awareness drives baseline accuracy on time-dependent flows but not uniformly across PDE types, that out-of-distribution parameter shifts reveal a universal vulnerability in complex 2D flows across all three architectures, and that physics-informed training is not a universal fix for spatially aware models. Finally, we profile computational efficiency, demonstrating that while the FNO provides predictable memory scaling, the unconstrained MLP remains unmatched for low-latency, real-time inference.
Self-Adaptive Physics-Informed Neural Networks (SA-PINNs) are a variation of traditional Physics-Informed Neural Networks (PINNs) designed to solve the challenges of solving ”stiff” partial differential equations (PDEs). By using adaptive weighting, SA-PINNs are able to focus their attention on areas of the domain with higher errors, therefore improving accuracy. This work investigates the roles of individual loss components, namely residuals, boundary conditions, and initial conditions, in the performance of SA-PINNs.
...
Self-Adaptive Physics-Informed Neural Networks (SA-PINNs) are a variation of traditional Physics-Informed Neural Networks (PINNs) designed to solve the challenges of solving ”stiff” partial differential equations (PDEs). By using adaptive weighting, SA-PINNs are able to focus their attention on areas of the domain with higher errors, therefore improving accuracy. This work investigates the roles of individual loss components, namely residuals, boundary conditions, and initial conditions, in the performance of SA-PINNs.