Pages that link to "Item:Q2020786"
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The following pages link to Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization (Q2020786):
Displaying 28 items.
- Learning ``best'' kernels from data in Gaussian process regression. With application to aerodynamics (Q2083686) (← links)
- Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model (Q2084108) (← links)
- Unsteady flow prediction from sparse measurements by compressed sensing reduced order modeling (Q2138820) (← links)
- Output-based adaptive aerodynamic simulations using convolutional neural networks (Q2245362) (← links)
- Bayesian neural networks for uncertainty quantification in data-driven materials modeling (Q2246265) (← links)
- A fast multi-fidelity method with uncertainty quantification for complex data correlations: application to vortex-induced vibrations of marine risers (Q2246346) (← links)
- Multi-fidelity optimization of super-cavitating hydrofoils (Q2310838) (← links)
- A modified variable complexity modeling for efficient multidisciplinary aircraft conceptual design (Q2357907) (← links)
- Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates (Q4997904) (← links)
- Training a Neural-Network-Based Surrogate Model for Aerodynamic Optimisation Using a Gaussian Process (Q5880409) (← links)
- Multifidelity deep operator networks for data-driven and physics-informed problems (Q6048427) (← links)
- Multi-fidelity surrogate-based optimal design of road vehicle suspension systems (Q6088558) (← links)
- Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry (Q6097587) (← links)
- GRIDS-Net: inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning (Q6116156) (← links)
- A fuzzy particle swarm optimization method with application to shape design problem (Q6140984) (← links)
- Neural-physics multi-fidelity model with active learning and uncertainty quantification for GPU-enabled microfluidic concentration gradient generator design (Q6153894) (← links)
- Multifidelity Surrogate Modeling for Time-Series Outputs (Q6164166) (← links)
- CNN-DP: composite neural network with differential propagation for impulsive nonlinear dynamics (Q6202143) (← links)
- Deep learning-enhanced efficient seismic analysis of structures with multi-fidelity modeling strategies (Q6202958) (← links)
- A multi-fidelity transfer learning strategy based on multi-channel fusion (Q6498489) (← links)
- Active learning-assisted multi-fidelity surrogate modeling based on geometric transformation (Q6550154) (← links)
- Machine learning based topology optimization of fiber orientation for variable stiffness composite structures (Q6554029) (← links)
- Complex nonlinear dynamics and vibration suppression of conceptual airfoil models: a state-of-the-art overview (Q6565143) (← links)
- AONN-2: an adjoint-oriented neural network method for PDE-constrained shape optimization (Q6572176) (← links)
- A deep learning -- genetic algorithm approach for aerodynamic inverse design via optimization of pressure distribution (Q6588345) (← links)
- Derivative-free bound-constrained optimization for solving structured problems with surrogate models (Q6641007) (← links)
- DiffMat: data-driven inverse design of energy-absorbing metamaterials using diffusion model (Q6643604) (← links)
- High-efficient sample point transform algorithm for large-scale complex optimization (Q6643612) (← links)
