Design-by-Morphing creates radical airfoil designs without geometric constraints.
problem Design constraints limit airfoil design novelty and small changes.
method Design-by-Morphing (DbM) creates a search space without geometric constraints.
result DbM generates radical airfoils with remarkable lift-over-drag ratio and stall angle tolerance.
New shape representation for airfoils improves design and manufacturing.
problem Designing and manufacturing airfoils efficiently and accurately.
method Combining physics-based and data-driven techniques on a Grassmannian manifold.
result Rich set of novel 2D airfoil deformations not previously captured.
Bézier-GAN optimizes airfoil design by reducing shape complexity.
problem High computational cost in aerodynamic shape optimization.
method Generative adversarial networks (GANs) to learn compact shape representations.
result Empirically accelerates optimization convergence by at least two times.
Deep learning improves accuracy of RANS simulations for airfoils.
problem Improving accuracy of Reynolds-Averaged Navier-Stokes simulations for airfoils.
method Used a modernized U-net architecture and evaluated various trained neural networks.
result Achieved a mean relative pressure and velocity error of less than 3% across various airfoil shapes.
Deep learning has been used in many areas, such as feature detections in images and the game of go. This paper presents a study that attempts to use the deep learning method to predict turbomachinery performance. Three different deep neural networks are built and trained to predict the pressure distributions of turbine…
The adaptability of the convolutional neural network (CNN) technique for aerodynamic meta-modeling tasks is probed in this work. The primary objective is to develop suitable CNN architecture for variable flow conditions and object geometry, in addition to identifying a sufficient data preparation process. Multiple CNN …
This paper designs sensor arrays for estimating unsteady flows efficiently.
problem Estimating high-dimensional unsteady flow fields with limited sensor placement.
method Combines data-driven modeling, Kalman Filter design, and sparsification for sensor selection.
result Proposed sensor arrays are highly effective for flow-field estimation across various conditions.
PropEn uses matching to create a larger dataset for efficient design optimization.
problem Limited data and complex landscapes in scientific applications.
method PropEn uses a matching approach to implicitly guide design without a discriminator.
result PropEn efficiently approximates the gradient of property improvement within the data distribution.
Paper learns optimal kernels for Gaussian process regression in aerodynamics.
problem Approximating complex functions from limited data in aerodynamics.
method Two algorithms: Kernel Flow and Spectral Kernel Ridge Regression.
result Explicit construction of optimal kernels based on target function features.
We develop an algorithm for minimizing a function using n batched function value measurements at each of T rounds by using classifiers to identify a function's sublevel set. We show that sufficiently accurate classifiers can achieve linear convergence rates, and show that the convergence rate is tied to the difficu…
BézierGAN generates smooth curves from low-dimensional parameters.
problem Designing smooth curves for aerodynamic and hydrodynamic shapes.
method Generative model that maps low-dimensional latent representation to Bézier curve points.
result Generates diverse and realistic curves with consistent shape variation.
MO-PaDGAN generates diverse, high-performance designs with multiple metrics.
problem Challenges in generating diverse, high-performance designs with multiple metrics.
method MO-PaDGAN uses a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances.
result MO-PaDGAN expands the design space towards high-performance regions and generates new designs with high diversity and performances.
We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural netw…
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introd…
New approach uses interpolation models and error bounds for verifiable scientific machine learning.
problem Challenges in verifying and validating modern scientific machine learning workflows.
method Combines multiple standard interpolation techniques with error bounds for efficient computation and comparative performance analysis.
result Error bounds for interpolation techniques can be computed or estimated efficiently, aiding in validation goals.
Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run,…
This work combines autoencoder transfer learning with MSCP for accurate aerodynamic predictions.
problem Data scarcity in aerodynamic modeling limits the use of high-fidelity simulations.
method Autoencoder-based transfer learning with MSCP for uncertainty-aware data fusion.
result The model achieves high accuracy with minimal high-fidelity training data and robust uncertainty bands.
Enhances inverse design optimization with machine learning and reduced fidelity simulations.
problem Limited compute resources in inverse design optimization.
method Synergy of multi-fidelity simulations, machine learning, and search space reduction.
result Significant computational resource savings and improved optimization performance.
Proposes MVGPR for spatiotemporal data modal analysis.
problem Sparse and irregularly sampled data in complex flows.
method Multivariate Gaussian process regression (MVGPR) with kernel design.
result MVGPR outperforms DMD and SPOD in modal analysis of sparse and irregular data.
Paper develops physics-informed, boundary-constrained Gaussian process for fluid flow field reconstruction.
problem Reconstructing fluid flow fields from limited data.
method Physics-informed, boundary-constrained Gaussian process regression.
result Derives physics-informed kernels for simulating incompressible flows.
Bayesian method maps high-dimensional inputs to lower dimensions for efficient multi-fidelity Gaussian Process modeling.
problem Efficiently modeling high-dimensional inputs with low-dimensional latent variables for multi-fidelity Gaussian Processes.
method Bayesian approach with orthonormal projection matrix inference using Markov Chain Monte Carlo (MCMC) and Geodesic Monte Carlo sampling.
result Optimal transformations identified that improve computational efficiency in multi-fidelity Gaussian Process modeling.
DAFNO learns surrogates for complex systems on irregular geometries.
problem Learning accurate surrogates for complex physical systems on irregular geometries.
method DAFNO incorporates a smoothed characteristic function in the integral layer architecture of FNOs, leveraging FFT for rapid computations.
result DAFNO achieves state-of-the-art accuracy on material modeling and airfoil simulation datasets.
MO-PaDGAN improves multi-objective optimization by generating diverse and high-performing designs.
problem Challenges in parameterizing engineering designs for multi-objective optimization.
method MO-PaDGAN uses a generative adversarial network with a Determinantal Point Processes loss function to address these challenges.
result MO-PaDGAN generates designs with improved performance and coverage, even surpassing training data.
Bayesian and POD methods fuse noisy wind tunnel and simulated aerodynamic data.
problem Fusing data from wind tunnel measurements and numerical simulations for accurate aerodynamic modeling.
method Bayesian and Proper Orthogonal Decomposition (POD) methods to infer true aerodynamic fields.
result Bayesian method is more robust with scarce data and accounts for uncertainties.
Proposes ICE-based metric for better understanding interactions in black-box models.
problem Misleading global sensitivity metrics in black-box models due to interaction effects.
method Individual Conditional Expectation (ICE) curves to compute feature importance and interactions.
result ICE-based metric provides richer insights into feature importance and interactions.
PaDGAN generates diverse, high-quality designs with improved performance.
problem Lack of diversity and performance improvement in generated designs.
method Integrates Determinantal Point Processes for diversity and quality, using GAN framework.
result PaDGAN generates higher quality designs with better diversity and without mode collapse.
GABI learns geometry from diverse systems to improve Bayesian inference.
problem Bayesian inversion of physical systems with varying geometries.
method Geometric Autoencoders for Bayesian Inversion (GABI) learns geometry-aware priors from large datasets.
result GABI yields comparable predictive accuracy to deterministic methods and well-calibrated uncertainty quantification.
ALMAB-DC optimizes expensive black-box experiments using active learning and distributed computing.
problem Efficiently optimizing expensive, gradient-free objectives in computational statistics and machine learning.
method Combines active learning, multi-armed bandits, and distributed asynchronous computing.
result Achieves lower simple regret and superior performance in various tasks compared to non-ALMAB baselines.