Observational Cosmology with Artificial Neural Networks

Olvera, Juan de Dios Rojas; Gómez-Vargas, Isidro; Vázquez, J. Alberto

doi:10.3390/universe8020120

Cited by 14 publications

(5 citation statements)

References 37 publications

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“…The output of a neuron is derived by calculating the weighted sum of the outputs of the neurons in the previous layer, as given in Eq. ( 1) [74]…”

Section: Neural Network Modelmentioning

confidence: 99%

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

Ürgün,

Yiğit,

Fidan

et al. 2024

Arab J Sci Eng

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This study fixates on determining the optimum laser input parameters that simultaneously meet the desired kerf width and depth during CO2 laser cutting of various polymethylmethacrylate (PMMA) sheets. It has three contributions. The first is to model the cutting process of PMMA by polynomial curve fitting as a function of laser power, laser speed, and standoff distance. R-squared (R2), adjusted R2 and root-mean-square error (RMSE) criteria were taken into account to measure the performance of the proposed model. The effect of laser parameters on the process is investigated by analysis of variance (ANOVA) and sensitivity analysis. The second is to optimize the derived nonlinear regression models using genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and ant lion optimization (ALO) metaheuristic methods and compare the performances of the algorithms. The third is to compare the adequacy of the optimization process with the artificial neural network (ANN). The investigations exhibited that the best-fitting polynomials are obtained with the R2 and adjusted R2 values of over 99% and 97%, respectively. ANOVA and sensitivity test revealed that the sensitivity of the laser power, which is the most effective parameter, was 150 at low powers and decreased to 0 as the power value increased. When the nozzle distance is 4.1, the proposed metaheuristics gave effective and sufficiently accurate results. PSO stood out in terms of both best cost value (3.49 × 10–8) and relative error value (0.19%). The relative error of the ANN was found as 3% in terms of kerf depth.

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“…The output of a neuron is derived by calculating the weighted sum of the outputs of the neurons in the previous layer, as given in Eq. ( 1) [74]…”

Section: Neural Network Modelmentioning

confidence: 99%

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

Ürgün,

Yiğit,

Fidan

et al. 2024

Arab J Sci Eng

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“…Ambiguity over the choice of kernels, the function dictionary, the mean function, and overfitting concerns with overwhelming errors in data-scarce regions have significantly limited the prospects of these approaches (Ó Colgáin & Sheikh-Jabbari 2021; Hwang et al 2023). This has led to an active use of deep learning with artificial neural networks (Wang et al 2020a(Wang et al , 2020bTang et al 2021;Escamilla-Rivera et al 2022;Olvera et al 2022;Dialektopoulos et al 2023Dialektopoulos et al , 2024Giambagli et al 2023;Gómez-Vargas et al 2023a, 2023bLiu et al 2023;Mehrabi 2023;Xie et al 2023;Zhang et al 2023Zhang et al , 2024Mukherjee et al 2024a) in this domain.…”

Section: Introductionmentioning

confidence: 99%

LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring Its Applications

Shah,

Saha,

Mukherjee

et al. 2024

ApJS

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We investigate the prospect of reconstructing the “cosmic distance ladder” of the Universe using a novel deep learning framework called LADDER—Learning Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on the apparent magnitude data from the Pantheon Type Ia supernova compilation, incorporating the full covariance information among data points, to produce predictions along with corresponding errors. After employing several validation tests with a number of deep learning models, we pick LADDER as the best-performing one. We then demonstrate applications of our method in the cosmological context, including serving as a model-independent tool for consistency checks for other data sets like baryon acoustic oscillations, calibration of high-redshift data sets such as gamma-ray bursts, and use as a model-independent mock-catalog generator for future probes. Our analysis advocates for careful consideration of machine learning techniques applied to cosmological contexts.

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“…With the accelerated development of computational resources, genetic algorithms and other machine learning algorithms have been exploited in several scientific fields in recent years. Remarkably, they have resulted in significant advances in understanding particle physics [32][33][34], astronomical information [35][36][37][38], and cosmological phenomena [39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Cosmological Parameter Estimation with Genetic Algorithms

Medel-Esquivel,

Gómez-Vargas,

Morales Sánchez

et al. 2023

Universe

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Genetic algorithms are a powerful tool in optimization for single and multimodal functions. This paper provides an overview of their fundamentals with some analytical examples. In addition, we explore how they can be used as a parameter estimation tool in cosmological models to maximize the likelihood function, complementing the analysis with the traditional Markov chain Monte Carlo methods. We analyze that genetic algorithms provide fast estimates by focusing on maximizing the likelihood function, although they cannot provide confidence regions with the same statistical meaning as Bayesian approaches. Moreover, we show that implementing sharing and niching techniques ensures an effective exploration of the parameter space, even in the presence of local optima, always helping to find the global optima. This approach is invaluable in the cosmological context, where an exhaustive space exploration of parameters is essential. We use dark energy models to exemplify the use of genetic algorithms in cosmological parameter estimation, including a multimodal problem, and we also show how to use the output of a genetic algorithm to obtain derived cosmological functions. This paper concludes that genetic algorithms are a handy tool within cosmological data analysis, without replacing the traditional Bayesian methods but providing different advantages.

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Observational Cosmology with Artificial Neural Networks

Cited by 14 publications

References 37 publications

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring Its Applications

Cosmological Parameter Estimation with Genetic Algorithms

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