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Register a new userOptimizing Startshot lightsail design: a generative network-based approach
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The Starshot lightsail project aims to build an ultralight spacecraft (nanocraft) that can reach Proxima Centauri b in approximately 20 years, requiring propulsion with a relativistic velocity of ~60 000 km/s. The spacecrafts acceleration approach currently under investigation is based on applying the radiation pressure from a high-power laser array located on Earth to the spacecraft lightsail. However, the practical realization of such a spacecraft imposes extreme requirements to the lightsails optical, mechanical, thermal properties. Within this work, we apply adjoint topology optimization and variational autoencoder-assisted inverse design algorithm to develop and optimize a silicon-based lightsail design. We demonstrate that the developed framework can provide optimized optical and opto-kinematic properties of the lightsail. Furthermore, the framework opens up the pathways to realizing a multi-objective optimization of the entire lightsail propulsion system, leveraging the previously demonstrated concept of physics-driven compressed space engineering
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Metasurfaces have enabled precise electromagnetic wave manipulation with strong potential to obtain unprecedented functionalities and multifunctional behavior in flat optical devices. These advantages in precision and functionality come at the cost of tremendous difficulty in finding individual meta-atom structures based on specific requirements (commonly formulated in terms of electromagnetic responses), which makes the design of multifunctional metasurfaces a key challenge in this field. In this paper, we present a Generative Adversarial Networks (GAN) that can tackle this problem and generate meta-atom/metasurface designs to meet multifunctional design goals. Unlike conventional trial-and-error or iterative optimization design methods, this new methodology produces on-demand free-form structures involving only a single design iteration. More importantly, the network structure and the robust training process are independent of the complexity of design objectives, making this approach ideal for multifunctional device design. Additionally, the ability of the network to generate distinct classes of structures with similar electromagnetic responses but different physical features could provide added latitude to accommodate other considerations such as fabrication constraints and tolerances. We demonstrate the networks ability to produce a variety of multifunctional metasurface designs by presenting a bifocal metalens, a polarization-multiplexed beam deflector, a polarization-multiplexed metalens and a polarization-independent metalens.
Light can exert forces on objects, promising to propel a meter-scale lightsail to near the speed of light. The key to address many challenges in such an ambition hinges on the nanostructuring of lightsails to tailor their optical scattering properties. In this letter, we present a first exhaustive study of photonic design of lightsails by applying large-scale optimization techniques to a generic geometry based on stacked photonic crystal layers. The optimization is performed by rigorous coupled-wave analysis amended with automatic differentiation methods for adjoint-variable gradient evaluations. Employing these methods the propulsion efficiency of a lightsail that involves a tradeoff between high broadband reflectivity and mass reduction is optimized. Surprisingly, regardless of the material choice, the optimal structures turn out to be simply one-dimensional subwavelength gratings, exhibiting nearly 50% improvement in acceleration distance performance compared to prior studies. Our framework can be extended to address other lightsail challenges such as thermal management and propulsion stability, and applications in integrated photonics such as compact mirrors.
We present a method for improving human design of chairs. The goal of the method is generating enormous chair candidates in order to facilitate human designer by creating sketches and 3d models accordingly based on the generated chair design. It consists of an image synthesis module, which learns the underlying distribution of training dataset, a super-resolution module, which improve quality of generated image and human involvements. Finally, we manually pick one of the generated candidates to create a real life chair for illustration.
Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a drastically different shape representation --- describing a shape as a sequence of computer-aided design (CAD) operations. Unlike meshes and point clouds, CAD models encode the user creation process of 3D shapes, widely used in numerous industrial and engineering design tasks. However, the sequential and irregular structure of CAD operations poses significant challenges for existing 3D generative models. Drawing an analogy between CAD operations and natural language, we propose a CAD generative network based on the Transformer. We demonstrate the performance of our model for both shape autoencoding and random shape generation. To train our network, we create a new CAD dataset consisting of 178,238 models and their CAD construction sequences. We have made this dataset publicly available to promote future research on this topic.
Nanowire arrays exhibit efficient light coupling and strong light trapping, making them well suited to solar cell applications. The processes that contribute to their absorption are interrelated and highly dispersive, so the only current method of optimizing the absorption is by intensive numerical calculations. We present an efficient alternative which depends solely on the wavelength-dependent refractive indices of the constituent materials. We choose each array parameter such that the number of modes propagating away from the absorber is minimized while the number of resonant modes within the absorber is maximized. From this we develop a semi-analytic method that quantitatively identifies the small range of parameters where arrays achieve maximum short circuit currents. This provides a fast route to optimizing NW array cell efficiencies by greatly reducing the geometries to study with full device models. Our approach is general and applies to a variety of materials and to a large range of array thicknesses.
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