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Found 7 papers, 1 papers with code
Understanding the training of PINNs for unsteady flow past a plunging foil through the lens of input subdomain level loss function gradients
To quantify which spatial zone drives the training, two novel metrics are computed from the zonal loss component gradient statistics and the proportion of sample points in each zone.
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
Gaussian processes (GPs) are a Bayesian machine learning approach widely used to construct surrogate models for the uncertainty quantification of computer simulation codes in industrial applications.
Physics-informed neural networks modeling for systems with moving immersed boundaries: application to an unsteady flow past a plunging foil
Hence, in the present work, an immersed boundary aware framework has been explored for developing surrogate models for unsteady flows past moving bodies.
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion
This is a challenge in multi-query contexts, where LES become prohibitively costly to deploy to understand how plume flow and tracer dispersion change with various atmospheric and source parameters.
Uncertainty quantification of a thrombosis model considering the clotting assay PFA-100
The analysis is performed using a polynomial chaos expansion as a parametric surrogate for the thrombosis model.
Physics-aware deep neural networks for surrogate modeling of turbulent natural convection
We show how it comes to play as a regularization close to the training boundaries which are zones of poor accuracy for standard PINNs and results in a noticeable global accuracy improvement at iso-budget.
