Related papers: Observational constraints using Bayesian statistic…
We introduce a physics-informed neural framework for modeling static and time-dependent galactic gravitational potentials. The method combines data-driven learning with embedded physical constraints to capture complex, small-scale features…
In this letter, we investigate the cosmic expansion scenario of the universe in the framework of $f(R,L_m)$ gravity theory. We consider a non-linear $f(R,L_m)$ model, specifically, $f(R,L_m)=\frac{R}{2}+L_m^n + \beta$, where $n$ and $\beta$…
Cosmography is an ideal tool to investigate the cosmic expansion history of the Universe in a model-independent way. The equations of motion in modified theories of gravity are usually very complicated; cosmography may select practical…
In recent years, with the development of quantum machine learning, quantum neural networks (QNNs) have gained increasing attention in the field of natural language processing (NLP) and have achieved a series of promising results. However,…
The overparameterization of variational quantum circuits, as a model of Quantum Neural Networks (QNN), not only improves their trainability but also serves as a method for evaluating the property of a given ansatz by investigating their…
We develop a novel deep learning method for uncertainty quantification in stochastic partial differential equations based on Bayesian neural network (BNN) and Hamiltonian Monte Carlo (HMC). A BNN efficiently learns the posterior…
Quantum state tomography (QST) faces exponential measurement requirements and noise sensitivity in multi-qubit systems, bottlenecking practical quantum technologies. We present a physics-informed neural network (PINN) framework integrating…
In this paper, we present an accelerating cosmological model by constraining the free parameters using the cosmological datasets in an extended symmetric teleparallel gravity for the flat and anisotropic space-time. We employ a time…
The contemporary cosmic expansion is considered in the context of Modified Friedmann Dynamics (MOFD). We discuss some relativistic model exploring analogy to MOND modification of Newtonian dynamics. We argue that MOFD cosmologies can…
A generalized mass-to-horizon entropy has recently been proposed as an extension of the Bekenstein-Hawking area law, derived from a modified mass-horizon relation constructed to ensure consistency with the Clausius equation. Within the…
We use cosmological constraints from current data sets and a figure of merit (FoM) approach to probe any deviations from general relativity (GR) at cosmological scales. The FoM approach is used to study the constraining power of various…
Data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present paper offers a…
This cosmological model is a study of modified $f(Q,T)$ theory of gravity which was recently proposed by Xu {\it et al.} (Eur. Phys. J. C {\bf 79}, 708 (2019)). In this theory of gravity, the action contains an arbitrary function $f(Q,T)$…
Constraining the dark energy deceleration parameter is one of the fascinating topics in the recent cosmological paradigm. This work aims to reconstruct the dark energy using parametrization of the deceleration parameter in a flat FRW…
Modified $f(R)$ gravity in the Palatini approach has been presently applied to Cosmology as a realistic alternative to dark energy. In this concern, a number of authors have searched for observational constraints on several $f(R)$ gravity…
We investigate the cosmological implications of $f(Q,C)$ gravity with $f(Q,C)=\alpha Q+\beta C$, where $Q$ is the non-metricity scalar and $C$ encapsulates cosmological expansion terms. Three parameterizations of the EoS for dark energy,…
We investigate two new observational perspectives in the context of torsional gravitational modification of general relativity, i.e., the $f(T)$ gravity: i) We use Pantheon data of type Ia supernovae motivated by a time variation of the…
In this paper, the cosmological model of the Universe has been presented in $f(Q)$ gravity and the parameters are constrained from the cosmological data sets. At the beginning, we have employed a well motivated form of $f(Q) = \alpha +…
Most cosmological data analysis today relies on the Friedmann-Lemaitre-Robertson-Walker (FLRW) metric, providing the basis of the current standard cosmological model. Within this framework, interesting tensions between our increasingly…
In this work, we propose using the mixture density network (MDN) to estimate cosmological parameters. We test the MDN method by constraining parameters of the $\Lambda$CDM and $w$CDM models using Type Ia supernovae and the power spectra of…