Search Results (4)

We present a novel machine learning (ML)-based method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditional root-finding techniques are computationally expensive, particularly for large-scale relativistic hydrodynamics simulations. To address this, we employ feedforward neural networks (NNC2PS and NNC2PL), trained in PyTorch (2.0+) and optimized for GPU inference using NVIDIA TensorRT (8.4.1), achieving significant speedups with minimal accuracy loss. The NNC2PS model achieves $L_1$ and $L_{\infty }$ errors of $4.54\times {10}^{-7}$ and $3.44\times {10}^{-6}$, respectively, while the NNC2PL model exhibits even lower error values. TensorRT optimization with mixed-precision deployment substantially accelerates performance compared to traditional root-finding methods. Specifically, the mixed-precision TensorRT engine for NNC2PS achieves inference speeds approximately 400 times faster than a traditional single-threaded CPU implementation for a dataset size of 1,000,000 points. Ideal parallelization across an entire compute node in the Delta supercomputer (dual AMD 64-core 2.45 GHz Milan processors and 8 NVIDIA A100 GPUs with 40 GB HBM2 RAM and NVLink) predicts a 25-fold speedup for TensorRT over an optimally parallelized numerical method when processing 8 million data points. Moreover, the ML method exhibits sub-linear scaling with increasing dataset sizes. We release the scientific software developed, enabling further validation and extension of our findings. By exploiting the underlying symmetries within the equation of state, these findings highlight the potential of ML, combined with GPU optimization and model quantization, to accelerate conservative-to-primitive inversion in relativistic hydrodynamics simulations. Full article

Z-grid finite volume models conserve all-scalar quantities as well as energy and potential enstrophy and yield better dispersion relations for shallow water equations than other finite volume models, such as C-grid and C-D grid models; however, they are more expensive to implement. During each time integration, a Z-grid model must solve Poisson equations to convert its vorticity and divergence to a stream function and velocity potential, respectively. To optimally utilize these conversions, we propose a model in which the stability and possibly accuracy on the sphere are improved by introducing more stencils, such that a generalized Z-grid model can utilize longer time-integration steps and reduce computing time. Further, we analyzed the proposed model’s dispersion relation and compared it to that of the original Z-grid model for a linearly rotating shallow water equation, an important property for numerical models solving primitive equations. The analysis results suggest a means of balancing stability and dispersion. Our numerical results also show that the proposed Z-grid model for a shallow water equation is more stable and efficient than the original Z-grid model, increasing the time steps by more than 1.4 times. Full article

To meet a growing demand for accurate high-fidelity vegetation cover mapping in urban areas toward biodiversity conservation and assessing the impact of climate change, this paper proposes a complete approach to species and vitality classification at single tree level by synergistic use of multimodality 3D remote sensing data. So far, airborne laser scanning system(ALS or airborne LiDAR) has shown promising results in tree cover mapping for urban areas. This paper analyzes the potential of mobile laser scanning system/mobile mapping system (MLS/MMS)-based methods for recognition of urban plant species and characterization of growth conditions using ultra-dense LiDAR point clouds and provides an objective comparison with the ALS-based methods. Firstly, to solve the extremely intensive computational burden caused by the classification of ultra-dense MLS data, a new method for the semantic labeling of LiDAR data in the urban road environment is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. These priors encode geometric primitives found in the scene through sample consensus segmentation. Then, single trees are segmented from the labelled tree points using the 3D graph cuts algorithm. Multinomial logistic regression classifiers are used to determine the fine deciduous urban tree species of conversation concern and their growth vitality. Finally, the weight-of-evidence (WofE) based decision fusion method is applied to combine the probability outputs of classification results from the MLS and ALS data. The experiment results obtained in city road corridors demonstrated that point cloud data acquired from the airborne platform achieved even slightly better results in terms of tree detection rate, tree species and vitality classification accuracy, although the tree vitality distribution in the test site is less balanced compared to the species distribution. When combined with MLS data, overall accuracies of 78% and 74% for tree species and vitality classification can be achieved, which has improved by 5.7% and 4.64% respectively compared to the usage of airborne data only. Full article

Although analysis of the genetic code has allowed explanations for its evolution to be proposed, little evidence exists in biochemistry and molecular biology to offer an explanation for the origin of the genetic code. In particular, two features of biology make the origin of the genetic code difficult to understand. First, nucleic acids are highly complicated polymers requiring numerous enzymes for biosynthesis. Secondly, proteins have a simple backbone with a set of 20 different amino acid side chains synthesized by a highly complicated ribosomal process in which mRNA sequences are read in triplets. Apparently, both nucleic acid and protein syntheses have extensive evolutionary histories. Supporting these processes is a complex metabolism and at the hub of metabolism are the carboxylic acid cycles. This paper advances the hypothesis that the earliest predecessor of the nucleic acids was a β-linked polyester made from malic acid, a highly conserved metabolite in the carboxylic acid cycles. In the β-linked polyester, the side chains are carboxylic acid groups capable of forming interstrand double hydrogen bonds. Evolution of the nucleic acids involved changes to the backbone and side chain of poly(β-d-malic acid). Conversion of the side chain carboxylic acid into a carboxamide or a longer side chain bearing a carboxamide group, allowed information polymers to form amide pairs between polyester chains. Aminoacylation of the hydroxyl groups of malic acid and its derivatives with simple amino acids such as glycine and alanine allowed coupling of polyester synthesis and protein synthesis. Use of polypeptides containing glycine and l-alanine for activation of two different monomers with either glycine or l-alanine allowed simple coded autocatalytic synthesis of polyesters and polypeptides and established the first genetic code. A primitive cell capable of supporting electron transport, thioester synthesis, reduction reactions, and synthesis of polyesters and polypeptides is proposed. The cell consists of an iron-sulfide particle enclosed by tholin, a heterogeneous organic material that is produced by Miller-Urey type experiments that simulate conditions on the early Earth. As the synthesis of nucleic acids evolved from β-linked polyesters, the singlet coding system for replication evolved into a four nucleotide/four amino acid process (AMP = aspartic acid, GMP = glycine, UMP = valine, CMP = alanine) and then into the triplet ribosomal process that permitted multiple copies of protein to be synthesized independent of replication. This hypothesis reconciles the “genetics first” and “metabolism first” approaches to the origin of life and explains why there are four bases in the genetic alphabet. Full article