Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection
The high expressivity and agility of physics-informed neural networks (PINNs) make them
promising candidates for full fluid flow PDE modeling. An important question is whether this …
promising candidates for full fluid flow PDE modeling. An important question is whether this …
Multi-scale approach for reliability-based design optimization with metamodel upscaling
For multi-scale materials, the interplay of material and design uncertainties and reliability-
based design optimization is complex and very dependent on the chosen modeling scale …
based design optimization is complex and very dependent on the chosen modeling scale …
Uncertainty Propagation in High-Dimensional Fields using Non-Intrusive Reduced Order Modeling and Polynomial Chaos
View Video Presentation: https://doi. org/10.2514/6.2023-1686. vid High-fidelity, physics-
based modeling and simulation have become integral to the design of aircraft, but can have …
based modeling and simulation have become integral to the design of aircraft, but can have …
Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion
Mapping near-field pollutant concentration is essential to track plume dispersion in urban
areas. By solving most of the turbulence spectrum, large-eddy simulations (LES) can …
areas. By solving most of the turbulence spectrum, large-eddy simulations (LES) can …
Physics-informed neural networks modelling for systems with moving immersed boundaries: Application to an unsteady flow past a plunging foil
Physics informed neural networks (PINNs) have been explored extensively in the recent
past for solving various forward and inverse problems for facilitating querying applications in …
past for solving various forward and inverse problems for facilitating querying applications in …
Contaminant source identification in an aquifer using a Bayesian framework with arbitrary polynomial chaos expansion
Stochastic methods are widely used for the identification of contaminant source information.
However, these methods suffer from low computational efficiency. To address this issue …
However, these methods suffer from low computational efficiency. To address this issue …
Uncertainty Quantification in Flows with Discontinuities: A Probabilistic Approach on Nonlinear Manifolds
View Video Presentation: https://doi. org/10.2514/6.2023-4092. vid Uncertainty quantification
(UQ) in transonic and supersonic flows is difficult due to the presence of strong …
(UQ) in transonic and supersonic flows is difficult due to the presence of strong …
High-Dimensional Uncertainty Propagation in Aerodynamics using Polynomial Chaos-Kriging
View Video Presentation: https://doi. org/10.2514/6.2023-3766. vid Uncertainty propagation
in simulations with high-dimensional outputs, such as computational fluid dynamics, is …
in simulations with high-dimensional outputs, such as computational fluid dynamics, is …
Optimizing identification of mine water inrush source with manifold reduction and semi-supervised learning using improved autoencoder
S Wang, P Zhai, X Yu, J Han, L Shi - Stochastic Environmental Research …, 2024 - Springer
To enhance the accuracy of identifying water sources in mine inrush incidents, this study,
taking the Shengquan coal mine in Shandong, China, as a case study, proposes a novel …
taking the Shengquan coal mine in Shandong, China, as a case study, proposes a novel …
Improved metamodels for predicting high-dimensional outputs by accounting for the dependence structure of the latent variables: application to marine flooding
Metamodelling techniques (also referred to as surrogate modelling) have shown high
performance to overcome the computational burden of numerical hydrodynamic models for …
performance to overcome the computational burden of numerical hydrodynamic models for …