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Register a new userIndicator patterns of forced change learned by an artificial neural network
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Many problems in climate science require the identification of signals obscured by both the noise of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to identify the year of input maps of temperature and precipitation from forced climate model simulations. This prediction task requires the ANN to learn forced patterns of change amidst a background of climate noise and model differences. We then apply a neural network visualization technique (layerwise relevance propagation) to visualize the spatial patterns that lead the ANN to successfully predict the year. These spatial patterns thus serve as reliable indicators of the forced change. The architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change. Results are compared to those of more standard approaches like signal-to-noise ratios and multi-linear regression in order to gain intuition about the reliable indicators identified by the ANN. We then apply an additional visualization tool (backward optimization) to highlight where disagreements in simulated and observed patterns of change are most important for the prediction of the year. This work demonstrates that ANNs and their visualization tools make a powerful pair for extracting climate patterns of forced change.
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Lightning casualties cause tremendous loss to life and property. However, very lately lightning has been considered as one of the major natural calamities which is now studied or monitored with proper instrumentation. The lightning characteristics over India have been studying by using daily data low resolution time series and monthly data high resolution monthly climatology. We have used ANN time series method (a neural network) to analyze the time series and defined which one will be the best predictor of lightning over India. The time series of lightning is output(dependent) and input (independent) are k-index, AOD, Cape etc. The Gaussian process regression, support vector machine, regression trees and linear regression defined the input variables. Which show approximately linear relation.
Artificial neural networks have diverged far from their early inspiration in neurology. In spite of their technological and commercial success, they have several shortcomings, most notably the need for a large number of training examples and the resulting computation resources required for iterative learning. Here we describe an approach to neurological network simulation, both architectural and algorithmic, that adheres more closely to established biological principles and overcomes some of the shortcomings of conventional networks.
The artificial neural networks (ANNs) have emerged with successful applications in nuclear physics as well as in many fields of science in recent years. In this paper, by using (ANNs), we have constructed a formula for the nuclear charge radii. Statistical modeling of nuclear charge radii by using ANNs has been seen as to be successful. Also, the charge radii, binding energies and two-neutron separation energies of Sn isotopes have been calculated by implementing of the new formula in Hartree-Fock-Bogoliubov (HFB) calculations. The results of the study shows that the new formula is useful for describing nuclear charge radii.
Prediction of material properties from first principles is often a computationally expensive task. Recently, artificial neural networks and other machine learning approaches have been successfully employed to obtain accurate models at a low computational cost by leveraging existing example data. Here, we present a software package Properties from Artificial Neural Network Architectures (PANNA) that provides a comprehensive toolkit for creating neural network models for atomistic systems. Besides the core routines for neural network training, it includes data parser, descriptor builder and force-field generator suitable for integration within molecular dynamics packages. PANNA offers a variety of activation and cost functions, regularization methods, as well as the possibility of using fully-connected networks with custom size for each atomic species. PANNA benefits from the optimization and hardware-flexibility of the underlying TensorFlow engine which allows it to be used on multiple CPU/GPU/TPU systems, making it possible to develop and optimize neural network models based on large datasets.
Multi-model projections in climate studies are performed to quantify uncertainty and improve reliability in climate projections. The challenging issue is that there is no unique way to obtain performance metrics, nor is there any consensus about which method would be the best method of combining models. The goal of this study was to investigate whether combining climate model projections by artificial neural network (ANN) approach could improve climate projections and therefore reduce the range of uncertainty. The equally-weighted model averaging (the mean model) and single climate model projections (the best model) were also considered as references for the ANN combination approach. Simulations of present-day climate and future projections from 15 General Circulation Models (GCMs) for temperature and precipitation were employed. Results indicated that combining GCM projections by the ANN combination approach significantly improved the simulations of present-day temperature and precipitation than the best model and the mean model. The identity of the best model changed between the two variables and among stations. Therefore, there was not a unique model which could represent the best model for all variables and/or stations over the study region. The mean model was also not skillful in giving a reliable projection of historical climate. Simulation of temperature indicated that the ANN approach had the best skill at simulating present-day monthly means than other approaches in all stations. Simulation of present-day precipitation, however, indicated that the ANN approach was not the best approach in all stations although it performed better than the mean model. Multi-model projections of future climate conditions performed by the ANN approach projected an increase in temperature and reduction in precipitation in all stations and for all scenarios.
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