Designing thermal radiation metamaterials via hybrid adversarial autoencoder and Bayesian optimization

• URL: http://arxiv.org/abs/2205.01063v1
• Date: Tue, 26 Apr 2022 06:50:13 GMT
• Title: Designing thermal radiation metamaterials via hybrid adversarial autoencoder and Bayesian optimization
• Authors: Dezhao Zhu, Jiang Guo, Gang Yu, C. Y. Zhao, Hong Wang, Shenghong Ju
• Abstract summary: We have developed a hybrid materials informatics approach to design narrowband thermal emitters at different target wavelengths. New structures with optimal properties can be quickly figured out in a compressed 2-dimensional latent space. The proposed design framework can be easily extended to other thermal radiation metamaterials design with higher dimensional features.
• Score: 19.684529604466015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objective. In this letter, we have developed a hybrid materials informatics approach which combines the adversarial autoencoder and Bayesian optimization to design narrowband thermal emitters at different target wavelengths. With only several hundreds of training data sets, new structures with optimal properties can be quickly figured out in a compressed 2-dimensional latent space. This enables the optimal design by calculating far less than 0.001\% of the total candidate structures, which greatly decreases the design period and cost. The proposed design framework can be easily extended to other thermal radiation metamaterials design with higher dimensional features.

Related papers

• Automated design of nonreciprocal thermal emitters via Bayesian optimization [4.255213032353507]
  Nonreciprocal thermal emitters that break Kirchhoff's law of thermal radiation promise exciting applications for thermal and energy applications. We present a general numerical approach to maximize the nonreciprocal effect. We choose doped magneto-optic materials and magnetic Weyl semimetal materials as model materials and focus on pattern-free multilayer structures.
  arXiv  Detail & Related papers  (2024-09-13T21:03:18Z)
• An Uncertainty-aware Deep Learning Framework-based Robust Design Optimization of Metamaterial Units [14.660705962826718]
  We propose a novel uncertainty-aware deep learning framework-based robust design approach for the design of metamaterial units. We demonstrate that the proposed design approach is capable of designing high-performance metamaterial units with high reliability.
  arXiv  Detail & Related papers  (2024-07-19T22:21:27Z)
• Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
  We identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis. We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
  arXiv  Detail & Related papers  (2024-03-26T16:33:12Z)
• Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
  We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization. We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons. Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
  arXiv  Detail & Related papers  (2024-03-20T00:41:12Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• Multi-fidelity Design of Porous Microstructures for Thermofluidic Applications [0.5249805590164902]
  Two-phase cooling methods enhanced by porous surfaces are emerging as potential solutions. In such porous structures, the optimum heat dissipation capacity relies on two competing objectives. We develop a data-driven framework for designing optimal porous microstructures for cooling applications.
  arXiv  Detail & Related papers  (2023-10-27T21:51:11Z)
• Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices [90.30316433184414]
  We propose a data-model-hardware tri-design framework for high- throughput, low-cost, and high-accuracy MOT on HD video stream. Compared to the state-of-the-art MOT baseline, our tri-design approach can achieve 12.5x latency reduction, 20.9x effective frame rate improvement, 5.83x lower power, and 9.78x better energy efficiency, without much accuracy drop.
  arXiv  Detail & Related papers  (2022-10-16T16:21:40Z)
• Generative Thermal Design Through Boundary Representation and Multi-Agent Cooperative Environment [0.0]
  We present a generative thermal design using cooperative multi-agent deep reinforcement learning and continuous geometric representation. The proposed framework consists of a pre-trained neural network surrogate model as an environment to predict heat transfer and pressure drop.
  arXiv  Detail & Related papers  (2022-08-16T21:22:44Z)
• Inverse design of photonic devices with strict foundry fabrication constraints [55.41644538483948]
  We introduce a new method for inverse design of nanophotonic devices which guarantees that designs satisfy strict length scale constraints. We demonstrate the performance and reliability of our method by designing several common integrated photonic components.
  arXiv  Detail & Related papers  (2022-01-31T02:27:25Z)
• How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning [82.67551367327634]
  We develop a new interpretable, multi-resolution machine learning framework for finding patterns in the unit-cells of materials. Specifically, we propose two new interpretable representations of metamaterials, called shape-frequency features and unit-cell templates.
  arXiv  Detail & Related papers  (2021-11-10T21:19:02Z)
• Inverse design of two-dimensional materials with invertible neural networks [0.6973491758935711]
  Inverse design framework (MatDesINNe) utilizing invertible neural networks can map both forward and reverse processes. We show framework can generate novel, high fidelity, and diverse candidates with near-chemical accuracy.
  arXiv  Detail & Related papers  (2021-06-06T02:49:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.