Yuanke Chen | Searcharxiv

arXiv2025

Spectrally-selective dynamic radiative thermoregulation via phase engineering

Maintaining comfortable temperatures for buildings, humans, and devices consumes a substantial portion of global energy, underscoring the urgent need for energy-efficient thermoregulation technologies. Dynamic radiative thermal emitters that can switch between passive cooling and heating modes offer a promising solution, but most existing devices exhibit broadband optical responses, resulting in unwanted parasitic heat exchange and limited performance. Here, we introduce an elegant strategy that uses a dielectric cap to transform broadband metal-insulator transition (MIT) materials into spectrally selective dynamic emitters. This design creates a highly tunable Fabry-Perot cavity, enabling a tailored thermal emission spectrum by engineering the reflected-wave phase profile. Our Fresnel-formalism-based phasor diagram analysis reveals two key routes for realizing high spectral selectivity: a high-index dielectric cap and a low-loss metallic MIT state, which are further validated by Bayesian optimization. Following this principle, we demonstrated a wide-angle spectrally-selective thermoregulator operating in the atmospheric transparency window (8-13 um), where the thermal emittance can be electrically tuned from about 0.2 to 0.9 through reversible copper electrodeposition on a germanium cavity. Furthermore, this strategy can be extended to multispectral electrochromic windows, enabling switching between solar heating and spectrally-selective radiative cooling. Our work establishes a versatile and generalizable paradigm for spectral engineering of dynamic thermal emitters, opening opportunities in energy-efficient buildings, wearable thermal comfort, spacecraft thermoregulation, and multispectral camouflage.

physics.optics↗

arXiv2018

Learning More Robust Features with Adversarial Training

In recent years, it has been found that neural networks can be easily fooled by adversarial examples, which is a potential safety hazard in some safety-critical applications. Many researchers have proposed various method to make neural networks more robust to white-box adversarial attacks, but an effective method have not been found so far. In this short paper, we focus on the robustness of the features learned by neural networks. We show that the features learned by neural networks are not robust, and find that the robustness of the learned features is closely related to the resistance against adversarial examples of neural networks. We also find that adversarial training against fast gradients sign method (FGSM) does not make the leaned features very robust, even if it can make the trained networks very resistant to FGSM attack. Then we propose a method, which can be seen as an extension of adversarial training, to train neural networks to learn more robust features. We perform experiments on MNIST and CIFAR-10 to evaluate our method, and the experiment results show that this method greatly improves the robustness of the learned features and the resistance to adversarial attacks.

cs.LG↗