Deep-learning assisted reduced order model for high-dimensional flow prediction from sparse data

The reconstruction and prediction of full-state flows from sparse data are of great scientific and engineering significance yet remain challenging, especially in applications where data are sparse and/or subjected to noise. To this end, this study proposes a deep-learning assisted non-intrusive reduced order model (named DCDMD) for high-dimensional flow prediction from sparse data. Based on the compressed sensing (CS)-Dynamic Mode Decomposition (DMD), the DCDMD model is distinguished by two novelties. Firstly, a sparse matrix is defined to overcome the strict random distribution condition of sensor locations in CS, thus allowing flexible sensor deployments and requiring very few sensors. Secondly, a deep-learning-based proxy is invoked to acquire coherent flow modes from the sparse data of high-dimensional flows, thereby addressing the issue of defining sparsity and the stringent incoherence condition in the conventional CSDMD. The two advantageous features, combined with the fact that the model retains flow physics in the online stage, lead to significant enhancements in accuracy and efficiency, as well as superior insensitivity to data noises (i.e., robustness), in both reconstruction and prediction of full-state flows. These are demonstrated by three benchmark examples, i.e., cylinder wake, weekly-mean sea surface temperature and isotropic turbulence in a periodic square area.Comment: 36 Pages, 23 Figures, 5 Table

Time History Extrapolation for FDTD Modeling of Shielding Enclosure Designs and EMI Antenna Geometries

The GPOF (generalized pencil-of-function) method was used to extrapolate the time response from FDTD simulations of EMI problems by approximating the time history as a sum of complex exponentials. This method can significantly shorten the FDTD program execution time. However, various difficulties can arise from parameterization during data-processing. The GPOF is applied to, and studied for, two relevant EMI problems, enclosure design and EMI antenna modeling. The merits of GPOF in modeling shielding enclosures and EMI antennas is evaluated through several example

FDTD Modeling of Lumped Ferrites

Implementing ferrites in finite-difference time-domain (FDTD) modeling requires special care because of the complex nature of the ferrite impedance. Considerable computational resources and time are required to directly implement a ferrite in the FDTD method. Fitting the ferrite impedance to an exponential series with the generalized-pencil-of-function (GPOF) method and using recursive convolution is an approach that minimizes the additional computational burden. An FDTD algorithm for a lumped ferrite using GPOF and recursive convolution is presented herein. Two different ferrite impedances in a test enclosure were studied experimentally to demonstrate the FDTD modeling approach. The agreement is generally good

Bis[bis­(2-ethyl-5-methyl-1H-imidazol-4-yl-κN 3)methane](nitrato-κ2 O,O′)nickel(II) nitrate

In the title compound, [Ni(NO3)(C13H20N4)2]NO3, the NiII ion shows a distorted octa­hedral geometry formed by four N atoms from two bis­(2-ethyl-5-methyl-1H-imidazol-4-yl)methane ligands and two O atoms from a chelating nitrate anion. Three ethyl groups in the complex cation and the O atoms of the uncoordinated nitrate anion are disordered over two sets of positions [occupancy ratios of 0.52 (3):0.48 (3) and 0.63 (3):0.37 (3), respectively]. In the crystal, inter­molecular N—H⋯O hydrogen bonds connect the complex cations into a zigzag chain along [010] and further N—H⋯O hydrogen bonds between the chains and the uncoordinated nitrate anions lead to layers parallel to (100)

Aqua­[bis­(2-ethyl-5-methyl-1H-imidazol-4-yl-κN 3)methane]­oxalatocopper(II) dihydrate

In the title compound, [Cu(C2O4)(C13H20N4)(H2O)]·2H2O, the CuII atom exhibits a distorted square-pyramidal geometry with the two N atoms of the imidazole ligand and the two O atoms of the oxalate ligand forming the basal plane, while the O atom of the coordinated water mol­ecule is in an apical position. The CuII atom is shifted 0.232 (2) Å out of the basal plane toward the water mol­ecule. The asymmetric unit is completed by two solvent water mol­ecules. These water mol­ecules participate in the formation of an intricate three-dimensionnal network of hydrogen bonds involving the coordinated water mol­ecule and the NH groups

GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest

Instruction tuning large language model (LLM) on image-text pairs has achieved unprecedented vision-language multimodal abilities. However, their vision-language alignments are only built on image-level, the lack of region-level alignment limits their advancements to fine-grained multimodal understanding. In this paper, we propose instruction tuning on region-of-interest. The key design is to reformulate the bounding box as the format of spatial instruction. The interleaved sequences of visual features extracted by the spatial instruction and the language embedding are input to LLM, and trained on the transformed region-text data in instruction tuning format. Our region-level vision-language model, termed as GPT4RoI, brings brand new conversational and interactive experience beyond image-level understanding. (1) Controllability: Users can interact with our model by both language and spatial instructions to flexibly adjust the detail level of the question. (2) Capacities: Our model supports not only single-region spatial instruction but also multi-region. This unlocks more region-level multimodal capacities such as detailed region caption and complex region reasoning. (3) Composition: Any off-the-shelf object detector can be a spatial instruction provider so as to mine informative object attributes from our model, like color, shape, material, action, relation to other objects, etc. The code, data, and demo can be found at https://github.com/jshilong/GPT4RoI.Comment: Code has been released at https://github.com/jshilong/GPT4Ro