Inverse Design of 2D Airfoils using Conditional Generative Models and Surrogate Log-Likelihoods

2021 ◽  
pp. 1-22
Author(s):  
Qiuyi Chen ◽  
Jun Wang ◽  
Phillip Pope ◽  
Wei (Wayne) Chen ◽  
Mark Fuge
Keyword(s):  
Optimal Transport ◽  
Generative Models ◽  
Inverse Design ◽  
Optimal Designs ◽  
Design Problems ◽  
Airfoil Optimization ◽  
Optimal Value ◽  
Design Paradigm ◽  
Low Dimensional ◽  
2D Airfoil

Abstract This paper shows how to use conditional generative models in 2D airfoil optimization to probabilistically predict good initialization points within the vicinity of the optima given the input boundary conditions, thus warm starting and accelerating further optimization. We accommodate the possibility of multiple optimal designs corresponding to the same input boundary condition and take this inversion ambiguity into account when designing our prediction framework. To this end, we first employ the conditional formulation of our previous work BezierGAN---Conditional BezierGAN (CBGAN)---as a baseline, then introduce its sibling conditional entropic BezierGAN (CEBGAN), which is based on optimal transport regularized with entropy. Compared with CBGAN, CEBGAN overcomes mode collapse plaguing conventional GANs, improves the average lift-drag (C_l/C_d) efficiency of airfoil predictions from 80.8% of the optimal value to 95.8%, and meanwhile accelerates the training process by 30.7%. Furthermore, we investigate the unique ability of CEBGAN to produce a log-likelihood lower bound that may help select generated samples of higher performance (e.g., aerodynamic performance). In addition, we provide insights into the performance differences between these two models with low-dimensional toy problems and visualizations. These results and the probabilistic formulation of this inverse problem justify the extension of our GAN-based inverse design paradigm to other inverse design problems or broader inverse problems.

scholarly journals Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ruichao Zhu ◽  
Tianshuo Qiu ◽  
Jiafu Wang ◽  
Sai Sui ◽  
Chenglong Hao ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Electromagnetic Waves ◽  
Inverse Method ◽  
Inverse Design ◽  
Learning Network ◽  
Design Paradigm ◽  
Simulation And Experiment ◽  
Meta Atom ◽  
Trained Network ◽  
Input Phase

AbstractMetasurfaces have provided unprecedented freedom for manipulating electromagnetic waves. In metasurface design, massive meta-atoms have to be optimized to produce the desired phase profiles, which is time-consuming and sometimes prohibitive. In this paper, we propose a fast accurate inverse method of designing functional metasurfaces based on transfer learning, which can generate metasurface patterns monolithically from input phase profiles for specific functions. A transfer learning network based on GoogLeNet-Inception-V3 can predict the phases of 28×8 meta-atoms with an accuracy of around 90%. This method is validated via functional metasurface design using the trained network. Metasurface patterns are generated monolithically for achieving two typical functionals, 2D focusing and abnormal reflection. Both simulation and experiment verify the high design accuracy. This method provides an inverse design paradigm for fast functional metasurface design, and can be readily used to establish a meta-atom library with full phase span.

scholarly journals Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES

2021 ◽  
Author(s):  
AkshatKumar Nigam ◽  
Robert Pollice ◽  
Mario Krenn ◽  
Gabriel dos Passos Gomes ◽  
Alán Aspuru-Guzik
Keyword(s):  
Chemical Space ◽  
Generative Models ◽  
Inverse Design

Interpolation and exploration within the chemical space for inverse design.

Modeling and Inverse Design of Convective Heat Transfer in a Grooved Channel Using Proper Orthogonal Decomposition

2009 ◽  
Author(s):  
Hasan Gunes ◽  
Sertac Cadirci
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Reynolds Number ◽  
Proper Orthogonal Decomposition ◽  
Convective Heat Transfer ◽  
Orthogonal Decomposition ◽  
Inverse Design ◽  
Heat Sources ◽  
Design Problems ◽  
Proper Orthogonal

In this study we show that the POD can be used as a useful tool to solve inverse design problems in thermo-fluids. In this respect, we consider a forced convection problem of air flow in a grooved channel with periodically mounted constant heat-flux heat sources. It represents a cooling problem in electronic equipments where the coolant is air. The cooling of electronic equipments with constant periodic heat sources is an important problem in the industry such that the maximum operating temperature must be kept below a value specified by the manufacturer. Geometric design in conjunction with the improved convective heat transfer characteristics is important to achieve an effective cooling. We obtain a model based on the proper orthogonal decomposition for the convection optimization problem such that for a given channel geometry and heat flux on the chip surface, we search for the minimum Reynolds number (i.e., inlet flow speed) for a specified maximum surface temperature. For a given geometry (l = 3.0 cm and h = 2.3 cm), we obtain a proper orthogonal decomposition (POD) model for the flow and heat transfer for Reynolds number in the range 1 and 230. It is shown that the POD model can accurately predict the flow and temperature field for off-design conditions and can be used effectively for inverse design problems.

Qualitative Knowledge and Manufacturing Considerations in Multidisciplinary Structural Optimization of Hybrid Material Structures

2006 ◽  
Vol 10 ◽  
pp. 143-152 ◽  
Author(s):  
Martin Huber ◽  
Horst Baier
Keyword(s):  
Hybrid Material ◽  
Optimization Problem ◽  
Fuzzy Rule ◽  
Optimal Designs ◽  
Design Parameters ◽  
Optimization Approach ◽  
Structural Level ◽  
Design Problems ◽  
Qualitative Knowledge ◽  
Typical Design

An optimization approach is derived from typical design problems of hybrid material structures, which provides the engineer with optimal designs. Complex geometries, different materials and manufacturing aspects are handled as design parameters using a genetic algorithm. To take qualitative information into account, fuzzy rule based systems are utilized in order to consider all relevant aspects in the optimization problem. This paper shows results for optimization tasks on component and structural level.

scholarly journals Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES

2021 ◽  
Author(s):  
AkshatKumar Nigam ◽  
Robert Pollice ◽  
Mario Krenn ◽  
Gabriel dos Passos Gomes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Chemical Space ◽  
Generative Models ◽  
Inverse Design ◽  
Learning Models ◽  
Structure Modification ◽  
Design Models ◽  
Comparable Performance ◽  
And Training

Inverse design allows the design of molecules with desirable properties using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. We achieve comparable performance on typical benchmarks without any training. We demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. We anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wide adoption.

scholarly journals Collective dynamics of repeated inference in variational autoencoder rapidly find cluster structure

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yoshihiro Nagano ◽  
Ryo Karakida ◽  
Masato Okada
Keyword(s):  
Activity Pattern ◽  
Latent Variables ◽  
Cluster Structure ◽  
Generative Models ◽  
Cluster Center ◽  
Specific Data ◽  
Global Cluster ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Low Dimensional

Abstract Deep neural networks are good at extracting low-dimensional subspaces (latent spaces) that represent the essential features inside a high-dimensional dataset. Deep generative models represented by variational autoencoders (VAEs) can generate and infer high-quality datasets, such as images. In particular, VAEs can eliminate the noise contained in an image by repeating the mapping between latent and data space. To clarify the mechanism of such denoising, we numerically analyzed how the activity pattern of trained networks changes in the latent space during inference. We considered the time development of the activity pattern for specific data as one trajectory in the latent space and investigated the collective behavior of these inference trajectories for many data. Our study revealed that when a cluster structure exists in the dataset, the trajectory rapidly approaches the center of the cluster. This behavior was qualitatively consistent with the concept retrieval reported in associative memory models. Additionally, the larger the noise contained in the data, the closer the trajectory was to a more global cluster. It was demonstrated that by increasing the number of the latent variables, the trend of the approach a cluster center can be enhanced, and the generalization ability of the VAE can be improved.

Scalable Set-Based Design Optimization and Remanufacturing for Meeting Changing Requirements

2020 ◽  
Author(s):  
Khalil Al Handawi ◽  
Petter Andersson ◽  
Massimo Panarotto ◽  
Ola Isaksson ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Optimal Designs ◽  
Design Problems ◽  
Direct Energy Deposition ◽  
Engineering Design Problems ◽  
Direct Energy ◽  
Transition Rules ◽  
Set Based Design ◽  
Manufacturing Technologies ◽  
Process Solutions

Abstract Engineering design problems often have open-ended requirements, especially in the early stages of development. Set-based design is a paradigm for exploring, and keeping under consideration, several alternatives so that commitment to a single design can be delayed until requirements are settled. In addition, requirements may change over the lifetime of a component or a system. Novel manufacturing technologies enable designs to be remanufactured to meet changed requirements. By considering this capability during the set-based design optimization process, solutions can be scaled to meet evolving requirements and customer specifications even after commitment. Such an ability can also support a circular economy paradigm based on the return of used or discarded components and systems to working condition. We propose a set-based design methodology to obtain scalable optimal solutions that can satisfy changing requirements through remanufacturing. We first use design optimization and surrogate modeling to obtain parametric optimal designs. This set of parametric optimal designs is then reduced to scalable optimal designs by observing a set of transition rules for the manufacturing process used (additive or subtractive). The methodology is demonstrated by means of a structural aeroengine component that is remanufactured by direct energy deposition of a stiffener to meet higher loading requirements.

scholarly journals Algorithmic lattice kirigami: A route to pluripotent materials

2015 ◽  
Vol 112 (24) ◽  
pp. 7449-7453 ◽  
Author(s):  
Daniel M. Sussman ◽  
Yigil Cho ◽  
Toen Castle ◽  
Xingting Gong ◽  
Euiyeon Jung ◽  
...  
Keyword(s):  
Flat Surface ◽  
Inverse Design ◽  
Honeycomb Lattice ◽  
Flat Sheet ◽  
Regular Arrangement ◽  
Stepped Surface ◽  
Design Paradigm ◽  
Complex Target ◽  
General Method

We use a regular arrangement of kirigami elements to demonstrate an inverse design paradigm for folding a flat surface into complex target configurations. We first present a scheme using arrays of disclination defect pairs on the dual to the honeycomb lattice; by arranging these defect pairs properly with respect to each other and choosing an appropriate fold pattern a target stepped surface can be designed. We then present a more general method that specifies a fixed lattice of kirigami cuts to be performed on a flat sheet. This single pluripotent lattice of cuts permits a wide variety of target surfaces to be programmed into the sheet by varying the folding directions.

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