arXiv subjects

Manish Yadav

Publications and source records attributed to Manish Yadav.

17 recordsLinked to original sources

arXiv2026

Testing a Sign-Switch Cosmological Model with Curvature through Latest Planck 2018, DESI DR2 and PantheonPlus\&SH0ES Observational Data

We investigate the spatial geometry of the Universe within the framework of a sign-switch dark energy scenario by extending the recently proposed $\Lambda_{\rm s}$CDM model to include a free curvature parameter $\Omega_k$.In this framework, the effective cosmological constant undergoes a transition from a negative to a positive value at a characteristic redshift $z_{\dagger}$. Using the latest Planck 2018 cosmic microwave background (CMB) data, DESI DR2 baryon acoustic oscillation (BAO) measurements, and the PantheonPlus\&SH0ES Type Ia supernova sample, we derive joint constraints on the spatial curvature parameter $\Omega_k$ and other cosmological parameters. We find that Planck data itself slightly favors a closed universe within both the $\Lambda_{\rm s}$CDM$+\Omega_k$ and $\Lambda$CDM$+\Omega_k$ frameworks, although spatial flatness remains well within the allowed uncertainties. When low-redshift probes were included, the curvature constraints were significantly tightened. In particular, the full Pk18+DR2+PP\&SH0ES dataset yields $\Omega_k = 0.0001 \pm 0.0014$ for the $\Lambda_{\rm s}$CDM model, indicating a universe that is remarkably consistent with spatial flatness. We further analyzed the correlations between $\Omega_k$, $H_0$, and $S_8$, finding that the inclusion of curvature and a sign-switch dark energy component helps stabilize cosmological parameter estimates while remaining compatible with current observational constraints. Model comparison using AIC and Bayesian evidence shows that the $\Lambda_{\rm s}$CDM model receives inconclusive/weak observational support relative to $\Lambda$CDM.

astro-ph.CO↗

arXiv2026

Emergent E-I Structure in Performance-Evolved Reservoir Networks of Neuronal Population Dynamics

Understanding how network structure gives rise to neuronal dynamics and whether compact computational models can recover that structure from data alone is a central challenge in computational neuroscience. We apply the performance-dependent network evolution (PDNE) framework to model the dynamics of the Wilson-Cowan (WC) neuronal system, a canonical two-population model of excitatory-inhibitory (E-I) interaction underlying physiological rhythms. Starting from a minimal seed network, PDNE iteratively grows and prunes a reservoir computing (RC) network based solely on prediction performance, yielding compact, task-optimized reservoirs networks. The evolved networks accurately predict both excitatory $E(t)$ and inhibitory $I(t)$ population activities across unseen stimulus amplitudes and generalize in a zero-shot manner to novel stimulus configurations: varying pulse number, position and amplitude without retraining. Structural analysis of the evolved networks reveals a consistent functional organization with nodes specialized for E, I, and shared E-I representations. Importantly, the population-level connectivity of the evolved reservoirs spontaneously recovers the correct excitatory-inhibitory sign pattern of the WC model for three of four interaction types, without this being imposed by design. These results demonstrate that performance-driven network evolution can produce not only accurate but structurally interpretable models of physiological rhythms, opening a path toward compact, data-efficient digital twins of neuronal systems.

nlin.AO↗

arXiv2026

Dynamical Dark Energy Signatures from a New Transition $Om(z)$ Parametrization in Flat FLRW Cosmology

We investigate a cosmic scenario using a new transition parameterization of the $Om(z)$ diagnostic, $Om(z) = \frac{z^l}{(1+z)^m}$, in the spatially flat Friedmann Lema\^itre Robertson-Walker (FLRW) framework. Using observational datasets such as Observational Hubble Data (OHD), Pantheon Plus (PP), and SH0ES, we analyze the evolution of the $Om(z)$ function to probe deviations from the standard $\Lambda$CDM model and constrain free parameter space {$H_0$, l, m } using Markov Chain Monte Carlo (MCMC) analysis with the emcee sampler. Our analysis reveals a clear transition in the slope of $Om(z)$ from negative to positive at transition redshift values $z_t \approx 1.41$, $0.65$, and $0.33$ for the OHD, OHD+PP, and OHD+PP$\&$SH0ES datasets, respectively. This behavior suggests a dynamical evolution of dark energy, indicating a transition from a quintessence-like phase to a phantom regime. From the combined OHD+PP$\&$SH0ES dataset, we obtain a best-fit value of the Hubble constant $ H_0 = 73.01 \pm 0.36 \, \mathrm{km\,s^{-1}\,Mpc^{-1}} $, which is consistent with the SH0ES calibration and supports the viability of our model. Additionally, our analysis indicates that the current age of the Universe is approximately $13 \sim 14$ Gyr from all available combinations of datasets, which is consistent with observational expectations. Further, we find that the deceleration-to-acceleration transition, which marks the beginning of cosmic acceleration, is inferred to occur within the redshift interval $z_t \in [0.5, 0.8]$, highlighting the emergence of dark energy as the dominant component in the Universe's recent expansion history. Our transition $Om(z)$ parameterization captured progressive cosmological changes and enabled seamless interpolation over cosmic epochs.

gr-qc↗

arXiv2026

Dynamical Oscillations in Dark Energy: Joint Constraints on the $w_{sin}$CDM Model from DESI, OHD, and Supernova Samples

In this study, we investigate the oscillatory dark energy model $w_{\sin}\mathrm{CDM}$ based on the DESI BAO data together with OHD, Pantheon Plus, and SH0ES measurements. We examine how the DESI data influence the dark energy equation-of-state plane $(w_0, w_a)$ within cosmological models that are free from Hubble tension and employ a Monte Carlo Markov Chain (MCMC) approach. Our findings indicate that although the parameter space still favors $w_a < 0$ and $w_0 > -1$ , the cosmological constant remains consistent with the DESI+OHD+PP combination at the $2\sigma$ level. We also observe that the best-fit Hubble constant $H_0$ is higher for the DESI+OHD+PP+SH0ES data combination, leading to a residual Hubble tension of less than $1\sigma$ to remain consistent with the SH0ES measurement. These results suggest that attempts to address the Hubble tension tend to reduce indication of DESI for the oscillatory dark energy model. Therefore, claims that the cosmological constant should be approached with greater caution, considering both the latest observational datasets and the existing cosmological tensions. We also obtained the present deceleration parameter and the effective equation-of-state value as $q_0 = -0.36$ and $w_{\mathrm{eff}} = -0.57$, respectively, for the DESI+OHD+PP+SH0ES dataset combination. Further analysis indicated a strong departure of $w_0$ from $w=-1$ at the $4\sigma$ level for the DR2+OHD+DES-5yr data combination. The inferred $\Omega_{m}$ tended to shift toward higher values when supernova samples were included, indicating a systematic preference for larger $\Omega_{m}$ in combinations involving supernova data.

physics.gen-ph↗

arXiv2025

Constraints on Spatial Curvature and Dark Energy Dynamics in the $w$CDM Model from DESI DR1 and DR2

In this study, we investigate the $w$CDM dynamical dark energy model with spatial curvature utilizing the recently released DESI Collaboration data (DR1 and DR2) in conjunction with other observational probes such as BBN, Observational Hubble Data (OHD), and Pantheon Plus (PP). Our investigation attempts to discover which DESI dataset gives a better match to the $w$CDM framework and assess the impact of spatial curvature on cosmological constraints. We find that the cosmic curvature parameter, $\Omega_k$, disfavors the cosmological constant for the DR2+BBN and DR2+BBN+OHD data combinations. However, the deviation from the cosmological constant remains below the $1\sigma$ level, indicating a mild preference for a open universe. In contrast, when using the DR1 based combinations namely DR1+BBN and DR1+BBN+OHD-the deviation from the cosmological constant increases to approximately $1.2\sigma$, suggesting a slightly stronger indication of a open geometry. Also, the best-fit values of the Hubble constant ($H_0$) obtained from the DR1+BBN+OHD+PP and DR2+BBN+OHD+PP combinations within the dynamical dark energy model are consistent with the results reported by the Planck Collaboration. Our findings provide constraints on the dark energy EoS parameter $ w_{\mathrm{}0}$, reveal a mild but notable deviation from the vacuum energy ($w = -1$) scenario at a significance level $1.8\sigma$ from DR2+BBN+OHD+PP and $0.5\sigma$ from DR1+BBN+OHD+PP, both favoring the quintessence region of dark energy. Furthermore, the key physical distance measures $D_H$, $D_V$, and $D_M$ show better consistency with our model when analyzed with the DR2 data.

astro-ph.CO↗

arXiv2025

Beyond $\Lambda$CDM: Exploring a Dynamical Cosmological Constant Framework Consistent with Late-Time Observations

In this work, we investigate a cosmological scenario with a time-dependent cosmological constant $\Lambda$(t) within the spatially flat Friedmann-Lema\^itre-Robertson-Walker (FLRW) framework. Here we study a power-law $\Lambda(t)$CDM model characterized by a dynamic cosmological constant expressed as a function of the Hubble parameter and its derivative $\Lambda(t)$ $=\alpha (\dot H+H^{2})+\lambda H^2+4\pi G\rho\eta.$ Using recent observational datasets (DESI BAO, OHD, and PP\&SH0ES), we constrain the model's free parameters $(H_{0},\alpha,\lambda,\eta)$ and analyze their impact on key cosmological quantities. A Markov chain Monte Carlo (MCMC) analysis of the best-fit value of $H_{0}=71.9\pm 0.23$ km/s/Mpc from PP\&SH0ES analysis only, which substantially alleviates the existing tension between early and late-time determinations of the Hubble constant, reducing it to $\sim1.5\sigma$. The reconstructed $Om$ diagnostic exhibits a negative slope, indicating a dynamic dark energy behavior with quintessence-like characteristics ($\omega>-1$). These results suggest that the proposed $\Lambda(t)$ model provides a viable alternative to the standard $\Lambda$CDM paradigm to explain the late-time acceleration of the universe. Our findings show that this model alleviates the Hubble tension more effectively than the standard $\Lambda$CDM . The model also demonstrates compatibility with late-time Hubble parameter observations and offers a compelling framework to address the limitations of $\Lambda$CDM.

gr-qc↗

arXiv2025

Empirical Validation: Investigating the $\Lambda_s $CDM Model with new DESI BAO Observations

The $\Lambda$CDM model has long served as the cornerstone of modern cosmology, offering an elegant and successful framework for interpreting a wide range of cosmological observations. However, the rise of high-precision datasets has revealed statistically significant tensions, most notably the Hubble tension and the $S_8$ discrepancy, which challenge the completeness of this standard model. In this context, we explore the $\Lambda_{\rm s}$CDM model-an extension of $\Lambda$CDM featuring a single additional parameter, $z_\dagger$, corresponding to a sign-switching cosmological constant. This minimal modification aims to alleviate key observational tensions without compromising the model's overall coherence. Recent findings present in the literature indicate that the $\Lambda_{\rm s}$CDM model not only provides a better fit to Lyman-$\alpha$ forest data for $z_\dagger < 2.3$, but also accommodates both the SH0ES measurement of $H_0$ and the angular diameter distance to the last scattering surface when 2D BAO data are included. We present a comprehensive analysis combining the full Planck 2018 CMB data, the Pantheon Type Ia Supernovae sample, and the recently released Baryon Acoustic Oscillation (BAO) measurements from the Dark Energy Spectroscopic Instrument (DESI). Our finding reveal that the Preliminary DESI results, a possible $3.9\sigma$ deviation from $\Lambda$CDM expectations, reinforce the importance of exploring such dynamic dark energy frameworks. In sum, our study underscores the potential of $\Lambda_{\rm s}$CDM to reconcile multiple cosmological tensions and sheds light on the role of upcoming high-precision observations in reshaping our understanding of the universe's expansion history and the nature of dark energy.

astro-ph.CO↗

arXiv2025

Cosmological Implications of a New Creation Field in Hoyle-Narlikar Gravity with Bulk Viscous Fluid

In this study, we present a comprehensive investigation of the Narlikar gravity model with bulk viscous fluid by the new foam of creation field $C(t) = t + \int \alpha (1 - a)dt + c_1$, based on the Hoyle-Narlikar's creation-field theory, using a joint analysis of Observational Hubble Data (OHD) and the Pantheon supernova (PP) compilation. Our analysis reveals that the creation field coupling constant $(f)$ is always positive within the Narlikar gravity model from OHD+PP data sets. The best-fit estimates yield $ H_0 = 71.2 \pm 2.1 \text{km s}^{-1}\text{Mpc}^{-1}$ and $\xi_0 = 0.23$, quoted at the $1 \sigma$ level. The Narlikar gravity model predicts a transition redshift of $z_t \approx 0.63$ marking the onset of late-time cosmic acceleration, with the corresponding age of the Universe estimated as $13.50\pm1.80 Gyr$. Interestingly, the inferred higher value of $H_0$, relative to SH0ES determinations, suggests a possible alleviation of the $\sim 4.1\sigma$ Hubble tension. Furthermore, we assess the stability of the model and demonstrate that the late-time acceleration can be consistently explained through the energy conditions. This model retains dynamical flexibility while ensuring analytical tractability and provides a promising framework to investigate the cosmological implications of Hoyle-Narlikar gravity, particularly regarding late-time acceleration and the evolution of dark energy.

gr-qc↗

arXiv2025

Node pruning reveals compact and optimal substructures within large networks

The structural complexity of reservoir networks poses a significant challenge, often leading to excessive computational costs and suboptimal performance. In this study, we introduce a systematic, task specific node pruning framework that enhances both the efficiency and adaptability of reservoir networks. By identifying and eliminating redundant nodes, we demonstrate that large networks can be compressed while preserving or even improving performance on key computational tasks. Our findings reveal the emergence of optimal subnetwork structures from larger Erdos Renyi random networks, indicating that efficiency is governed not merely by size but by topological organization. A detailed analysis of network structure at both global and node levels uncovers the role of density distributions, special-radius and asymmetric input-output node distributions, among other graph-theoretic measures that enhance the computational capacity of pruned compact networks. We show that pruning leads to non-uniform network refinements, where specific nodes and connectivity patterns become critical for information flow and memory retention. This work offers fundamental insights into how structural optimization influences reservoir dynamics, providing a pathway toward designing more efficient, scalable, and interpretable machine learning architectures.

physics.comp-ph↗

arXiv2025

Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing

Measurements acquired from distributed physical systems are often sparse and noisy. Therefore, signal processing and system identification tools are required to mitigate noise effects and reconstruct unobserved dynamics from limited sensor data. However, this process is particularly challenging because the fundamental equations governing the dynamics are largely unavailable in practice. Reservoir Computing (RC) techniques have shown promise in efficiently simulating dynamical systems through an unstructured and efficient computation graph comprising a set of neurons with random connectivity. However, the potential of RC to operate in noisy regimes and distinguish noise from the primary smooth or non-smooth deterministic dynamics of the system has not been fully explored. This paper presents a novel RC method for noise filtering and reconstructing unobserved nonlinear dynamics, offering a novel learning protocol associated with hyperparameter optimization. The performance of the RC in terms of noise intensity, noise frequency content, and drastic shifts in dynamical parameters is studied in two illustrative examples involving the nonlinear dynamics of the Lorenz attractor and the adaptive exponential integrate-and-fire system. It is demonstrated that denoising performance improves by truncating redundant nodes and edges of the reservoir, as well as by properly optimizing hyperparameters, such as the leakage rate, spectral radius, input connectivity, and ridge regression parameter. Furthermore, the presented framework shows good generalization behavior when tested for reconstructing unseen and qualitatively different attractors. Compared to the extended Kalman filter, the presented RC framework yields competitive accuracy at low signal-to-noise ratios and high-frequency ranges.

cs.LG↗

arXiv2024

The impact of AI on engineering design procedures for dynamical systems

Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design process, paving the way for the adoption of AI-based methods. In this paper, we examine the potential for integrating AI into the engineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as a foundation. We identify and classify AI methods based on their suitability for specific stages within the engineering product design workflow. Furthermore, we present a series of application examples where AI-assisted design has been successfully implemented by the authors. These examples, drawn from research projects within the DFG Priority Program \emph{SPP~2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics}, showcase a diverse range of applications across mechanics and mechatronics, including areas such as acoustics and robotics.

eess.SY↗

arXiv2024

Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design

Data-based methods have gained increasing importance in engineering, especially but not only driven by successes with deep artificial neural networks. Success stories are prevalent, e.g., in areas such as data-driven modeling, control and automation, as well as surrogate modeling for accelerated simulation. Beyond engineering, generative and large-language models are increasingly helping with tasks that, previously, were solely associated with creative human processes. Thus, it seems timely to seek artificial-intelligence-support for engineering design tasks to automate, help with, or accelerate purpose-built designs of engineering systems, e.g., in mechanics and dynamics, where design so far requires a lot of specialized knowledge. However, research-wise, compared to established, predominantly first-principles-based methods, the datasets used for training, validation, and test become an almost inherent part of the overall methodology. Thus, data publishing becomes just as important in (data-driven) engineering science as appropriate descriptions of conventional methodology in publications in the past. This article analyzes the value and challenges of data publishing in mechanics and dynamics, in particular regarding engineering design tasks, showing that the latter raise also challenges and considerations not typical in fields where data-driven methods have been booming originally. Possible ways to deal with these challenges are discussed and a set of examples from across different design problems shows how data publishing can be put into practice. The analysis, discussions, and examples are based on the research experience made in a priority program of the German research foundation focusing on research on artificially intelligent design assistants in mechanics and dynamics.

cs.CY↗

arXiv2024

Predicting multi-parametric dynamics of externally forced oscillator using reservoir computing and minimal data

Mechanical systems exhibit complex dynamical behavior from harmonic oscillations to chaotic motion. The dynamics undergo qualitative changes due to changes to internal system parameters like stiffness and changes to external forcing. Mapping out complete bifurcation diagrams numerically or experimentally is resource-consuming, or even infeasible. This study uses a data-driven approach to investigate how bifurcations can be learned from a few system response measurements. Particularly, the concept of reservoir computing (RC) is employed. As proof of concept, a minimal training dataset under the resource constraint problem of a Duffing oscillator with harmonic external forcing is provided as training data. Our results indicate that the RC not only learns to represent the system dynamics for the external forcing seen during training, but it also provides qualitatively accurate and robust system response predictions for completely unknown multi-parameter regimes outside the training data. Particularly, while being trained solely on regular period-2 cycle dynamics, the proposed framework correctly predicts higher-order periodic and even chaotic dynamics for out-of-distribution forcing signals.

nlin.CD↗

arXiv2024

Testing spatial curvature in an anisotropic extension of $w$CDM model with low redshift data

In this letter, we report the observational constraints on a Bianchi type I anisotropic extension of $w$CDM model with spatial curvature from observational data including Baryon Acoustic Oscillations (BAO), Cosmic chronometers (CC), Big Bang nucleosynthesis (BBN), Pantheon+ (PP) compilation of SNe Ia and SH0ES Cepheid host distance anchors. The anisotropy is found to be of the order $10^{-13}$, which interplay with spatial curvature to reduce $H_0$ tension by $\sim 1\sigma$ as found in the analyses with BAO+CC+BBN+PP combination of data, while no significant effect of anisotropy is observed with BAO+CC+BBN+PPSH0ES combination of data. A closed Universe is favored by $w$CDM as well as anisotropic $w$CDM models with spatial curvature in analyses with BAO+CC+BBN+PP combination of data. An observation of an open Universe from $w$CDM model with spatial curvature in analyses with BAO+CC+BBN+PPSH0ES combination of data and a closed Universe from anisotropic $w$CDM model with curvature in analyses with same combination of data is made. The quintessence form of dark energy is favored at 95\% CL in both analyses.

astro-ph.CO↗

arXiv2024

Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity

The quest to understand structure-function relationships in networks across scientific disciplines has intensified. However, the optimal network architecture remains elusive, particularly for complex information processing. Therefore, we investigate how optimal and specific network structures form to efficiently solve distinct tasks using a novel framework of performance-dependent network evolution, leveraging reservoir computing principles. Our study demonstrates that task-specific minimal network structures obtained through this framework consistently outperform networks generated by alternative growth strategies and Erdős-Rényi random networks. Evolved networks exhibit unexpected sparsity and adhere to scaling laws in node-density space while showcasing a distinctive asymmetry in input and information readout nodes distribution. Consequently, we propose a heuristic for quantifying task complexity from performance-dependently evolved networks, offering valuable insights into the evolutionary dynamics of network structure-function relationships. Our findings not only advance the fundamental understanding of process-specific network evolution but also shed light on the design and optimization of complex information processing mechanisms, notably in machine learning.

nlin.AO↗

arXiv2019

Dynamic Partition Bloom Filters: A Bounded False Positive Solution For Dynamic Set Membership (Extended Abstract)

Dynamic Bloom filters (DBF) were proposed by Guo et. al. in 2010 to tackle the situation where the size of the set to be stored compactly is not known in advance or can change during the course of the application. We propose a novel competitor to DBF with the following important property that DBF is not able to achieve: our structure is able to maintain a bound on the false positive rate for the set membership query across all possible sizes of sets that are stored in it. The new data structure we propose is a dynamic structure that we call Dynamic Partition Bloom filter (DPBF). DPBF is based on our novel concept of a Bloom partition tree which is a tree structure with standard Bloom filters at the leaves. DPBF is superior to standard Bloom filters because it can efficiently handle a large number of unions and intersections of sets of different sizes while controlling the false positive rate. This makes DPBF the first structure to do so to the best of our knowledge. We provide theoretical bounds comparing the false positive probability of DPBF to DBF.

cs.DS↗

arXiv2018

Environment induced Symmetry Breaking of the Oscillation-Death State

We investigate the impact of a common external system, which we call a common environment, on the Oscillator Death (OD) states of a group of Stuart-Landau oscillators. The group of oscillators yield a completely symmetric OD state when uncoupled to the external system, i.e. the two OD states occur with equal probability. However, remarkably, when coupled to a common external system this symmetry is significantly broken. For exponentially decaying external systems, the symmetry breaking is very pronounced for low environmental damping and strong oscillator-environment coupling. This is evident through the sharp transition from the symmetric to asymmetric state occurring at a critical oscillator-environment coupling strength and environmental damping rate. Further, we consider time-varying connections to the common external environment, with a fraction of oscillator-environment links switching on and off. Interestingly, we find that the asymmetry induced by environmental coupling decreases as a power law with increase in fraction of such on-off connections. The suggests that blinking oscillator-environment links can restore the symmetry of the OD state. Lastly, we demonstrate the generality of our results for a constant external drive, and find marked breaking of symmetry in the OD states there as well. When the constant environmental drive is large, the asymmetry in the OD states is very large, and the transition between the symmetric and asymmetric state with increasing oscillator-environment coupling is very sharp. So our results demonstrate an environmental coupling-induced mechanism for the prevalence of certain OD states in a system of oscillators, and suggests an underlying process for obtaining certain states preferentially in ensembles of oscillators with environment-mediated coupling.

nlin.AO↗