深度神经算子驱动的异构场下多功能超材料的并发多任务设计
Deep Neural Operator Enabled Concurrent Multitask Design for Multifunctional Metamaterials under Heterogeneous Fields
AI总结：
针对多功能超材料异构场逆向设计难题，提出基于隐式傅里叶神经算子的数据驱动框架，通过多任务并发优化和傅里叶多类混合技术，实现了帕累托最优的架构-激励对设计，并在可编程等离激元纳米天线上验证了有效性。
摘要：
多功能超材料（MMM）作为支持微型化和定制化的下一代材料平台具有广阔前景。尽管已有许多概念验证展示且深度学习辅助设计不断涌现，但MMM的逆向设计，特别是涉及异构场且可能受元原子间相互耦合或长程相互作用影响的设计，仍 largely under-explored。为此，我们提出了一种数据驱动的设计框架，简化了涉及异构场的MMM逆向设计。其核心支撑是隐式傅里叶神经算子（IFNO），它以参数高效和样本高效的方式预测分布在超材料阵列上的异构场，这些场通常与均匀化假设不符。此外，我们提出了涵盖广泛MMM类别的逆问题标准表述，以及基于梯度的多任务并发优化，用于识别一组帕累托最优的架构-激励（A-S）对。我们提出傅里叶多类混合方法来合成锚定在一组几何基元上的类间元原子，同时享受免训练的降维和内置重建能力。三大支柱相互锁合，该框架在逐光可编程的等离激元纳米天线上得到验证，其设计涉及由准自由形式超晶胞、可操纵的入射相位分布以及涉及按需定位模式的冲突品质因数共同跨越的广阔空间。该框架在无先验简化的情况下应对所有挑战，有望推动MMM的未来发展。
英文摘要：
Multifunctional metamaterials (MMM) bear promise as next-generation material platforms supporting miniaturization and customization. Despite many proof-of-concept demonstrations and the proliferation of deep learning assisted design, grand challenges of inverse design for MMM, especially those involving heterogeneous fields possibly subject to either mutual meta-atom coupling or long-range interactions, remain largely under-explored. To this end, we present a data-driven design framework, which streamlines the inverse design of MMMs involving heterogeneous fields. A core enabler is implicit Fourier neural operator (IFNO), which predicts heterogeneous fields distributed across a metamaterial array, thus in general at odds with homogenization assumptions, in a parameter-/sample-efficient fashion. Additionally, we propose a standard formulation of inverse problem covering a broad class of MMMs, and gradient-based multitask concurrent optimization identifying a set of Pareto-optimal architecture-stimulus (A-S) pairs. Fourier multiclass blending is proposed to synthesize inter-class meta-atoms anchored on a set of geometric motifs, while enjoying training-free dimension reduction and built-it reconstruction. Interlocking the three pillars, the framework is validated for light-bylight programmable plasmonic nanoantenna, whose design involves vast space jointly spanned by quasi-freeform supercells, maneuverable incident phase distributions, and conflicting figure-of-merits involving on-demand localization patterns. Accommodating all the challenges without a-priori simplifications, our framework could propel future advancements of MMM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
