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Abstract Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series. 

Abstract Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling. 

Abstract Wide‐bandgap semiconductors (WBGS) with energy bandgaps larger than 3.4 eV for GaN and 3.2 eV for SiC have gained attention for their superior electrical and thermal properties, which enable high‐power, high‐frequency, and harsh‐environment devices beyond the capabilities of conventional semiconductors. Pushing the potential of WBGS boundaries, current research is redefining the field by broadening the material landscape and pioneering sophisticated synthesis techniques tailored for state‐of‐the‐art device architectures. Efforts include the growth of freestanding nanomembranes, the leveraging of unique interfaces such as van der Waals (vdW) heterostructure, and the integration of 2D with 3D materials. This review covers recent advances in the synthesis and applications of freestanding WBGS nanomembranes, from 2D to 3D materials. Growth techniques for WBGS, such as liquid metal and epitaxial methods with vdW interfaces, are discussed, and the role of layer lift‐off processes for producing freestanding nanomembranes is investigated. The review further delves into electronic devices, including field‐effect transistors and high‐electron‐mobility transistors, and optoelectronic devices, such as photodetectors and light‐emitting diodes, enabled by freestanding WBGS nanomembranes. Finally, this review explores new avenues for research, highlighting emerging opportunities and addressing key challenges that will shape the future of the field. 

Abstract Freestanding single‐crystalline nanomembranes have gained increasing attention as promising platforms for both fundamental research and advanced electronic applications. However, internal stress gradients arising from epitaxial strain within the oxide membranes often result in high crack density during fabrication, leading to unsatisfactory yield and limited reliability. Here, an elastically graded polymer (EGP) support that enables wafer‐scale crack‐free transfer of single‐crystalline oxide membranes are developed. The engineered elastic gradient within the EGP accommodates the internal strain of the oxide membrane, effectively minimizing crack formation during lift‐off. Notably, this ability to spatially control the interfacial stiffness between the polymer and the oxide film enables crack suppression under both tensile and compressive strain. This approach provides a robust and scalable route to producing high‐quality freestanding oxide membranes, paving the way not only for their integration into novel device architecture but also opening new avenues for scientific exploration of functional systems.