arXiv:2602. 08406v2 Announce Type: replace-cross Abstract: The prediction of electromagnetic spectra for MXene-based solar absorbers, where MXenes are a family of two-dimensional transition metal carbides and nitrides, is a computationally intensive task traditionally addressed using full-wave solvers.
By Shujaat Khan, Waleed Iqbal Waseer, Muhammad Shahid Jabbar
Metasurfaces enable precise manipulation of electromagnetic waves for applications such as beam steering, sensing, and stealth technology. However, inverse design of metasurfaces with targeted EM responses remains challenging due to the computational expense of iterative full wave simulation driven optimization and the limited conditioning fidelity and diversity of existing generative approaches.
arXiv:2608. 17344v1 Announce Type: cross Abstract: Neural network surrogates are an emerging alternative to traditional electromagnetic wave simulators like finite-difference time-domain (FDTD); their goal is to replace rigorous physical simulations with pre-trained neural networks that solve wave-scattering forward and inverse problems orders of magnitude faster.
By Charles Dove, Laura Waller
arXiv:2604. 02429v2 Announce Type: replace-cross Abstract: Convolutional neural networks (CNNs) have transformed image processing, but the energy consumption and inference latency of electronic based implementations remain fundamental bottlenecks.
By Saurabh Ranjan, Sonika Thakral, Amit Sehgal
arXiv:2606. 09266v1 Announce Type: cross Abstract: Acoustic metamaterial (AMM) inverse design is particularly challenging for broadband target responses due to acoustic dispersion: a structure that matches the desired response at one frequency may deviate at others, and modifying geometry to improve one sub-band often perturbs neighboring sub-bands.
By Yijie Li, Jiahao Xu, Ching-Chih Tsao, Lili Qiu, Jingxian Wang
arXiv:2607. 08392v1 Announce Type: cross Abstract: Amortized neural inverse design typically remains closed-world: component choices are fixed vocabulary tokens, coordinate grids are frozen at training time, and continuous variables are discretized into sequence tokens.
By Zhiyi Li, Yuheng Jin, Yidan Huang, Nan Chen, Hongyan Fu, Yikun Bu
arXiv:2608. 09382v1 Announce Type: cross Abstract: Electromagnetic inverse scattering is a nonlinear and ill-posed problem, where accurate reconstruction is challenging due to measurement limitations, noise, and high computational costs, especially for 3-D imaging.
By Yutong Du, Zicheng Liu, Bo Qi, Yali Zong, Peixian Han
arXiv:2604. 07421v3 Announce Type: replace Abstract: Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill-posed.
By Zhenyu Wang, Peiyuan Li, Yongxiang Shi, Ruoyu Wu, Chenfei Liao, Lei Zhang
arXiv:2607. 24785v1 Announce Type: cross Abstract: Efficient exploration of the photonic crystal (PhC) lattice design space is essential for developing photonic crystal surface-emitting lasers.
By Cen Chen, Haitao Huang, Jiazhi Mao, Feifan Xu, Zhe Zhuang, Yuxiang Ren
arXiv:2511. 13020v2 Announce Type: replace-cross Abstract: Although hyperspectral imaging offers unparalleled non-invasive physiological insight, its bulky hardware, slow acquisition, and regulatory burden severely limit its clinical availability.
By Yufei Wen, Shuxing Zhong, Jingdan Kang, Yuting Zhang, Jintai Chen, Kaishun Wu
arXiv:2606. 03936v1 Announce Type: new Abstract: Neural operator surrogates (NO) approximate PDE solutions orders of magnitude faster than numerical solvers, but suffer from spectral bias: high-frequency content is systematically attenuated, limiting reliability where fine-scale structure matters.
By Niccol\`o Perrone, Fanny Lehmann, Stefania Fresca, Filippo Gatti
arXiv:2608. 11585v1 Announce Type: cross Abstract: Physical computing systems exploit device dynamics for computation, but their gradient-based optimization is challenging: backpropagation through a digital twin suffers from model-reality gap.
By Cyrill B\"osch, Yigithan Gediz, Hakan T\"ureci