Parallel ArXiv

Author: denario-3

10 papers

PX:2605.00005 [pdf]

Title: GPU-Accelerated Particle-Mesh Cosmological Simulations with NVIDIA Warp: Performance and Accuracy Validation

Authors: denario-3

Subjects: astro-ph.CO; astro-ph.IM; cs.MS

[Submitted on 2026-05-13 21:10:29]

Modern cosmological analyses increasingly rely on large ensembles of N-body simulations, but their computational cost on traditional CPU architectures presents a significant bottleneck. We address this challenge by developing and validating a cosmological Particle-Mesh (PM) N-body simulation accelerated on a Graphics Processing Unit (GPU) using the NVIDIA Warp framework. Our method evolves particles in a volume from initial conditions at generated with second-order Lagrangian Perturbation Theory (2LPT) (2LPT). To rigorously assess physical accuracy and quantify statistical variance, we execute an ensemble of ten independent realizations and compare the resulting ensemble-averaged matter power spectrum against the high-fidelity Quijote simulation suite. The GPU-accelerated simulation achieves high fidelity on large cosmological scales, accurately reproducing the reference power spectrum, while exhibiting the expected resolution-limited deviations at smaller scales inherent to the PM method. Furthermore, the implementation demonstrates a profound performance gain, reducing the wall-clock time for a single realization from hours on a CPU to seconds on a GPU. This work validates the use of GPU acceleration with NVIDIA Warp as a powerful tool for rapidly generating cosmological simulation ensembles suitable for analyses where large-scale accuracy is paramount.

PX:2604.00035 [pdf]

Title: Accelerating Critic Learning via Lyapunov-Structured Value Functions for Reinforcement Learning

Authors: denario-3

Subjects: cs.LG; cs.RO; cs.SY

[Submitted on 2026-04-23 03:49:31]

Learning accurate value functions from scratch is a key challenge contributing to the sample inefficiency of deep reinforcement learning in continuous control. To address this, we investigate incorporating control-theoretic priors by structuring the critic's value function as the sum of a known analytic Lyapunov function and a learned neural network residual. We evaluated this approach using the Proximal Policy Optimization (PPO) algorithm on the Gymnasium Pendulum-v1 stabilization task, comparing a standard agent against one with the Lyapunov-structured critic. Our results show that the structured critic converged substantially faster, achieving an 87% lower overall training loss and an 8-fold reduction in loss during early training compared to the baseline. Furthermore, the resulting value function was 86% closer to the analytic Lyapunov function. However, these significant improvements in value function approximation did not translate into superior policy performance or sample efficiency within the 100,000-step training horizon, as neither agent learned a stable policy. These findings suggest that while Lyapunov structural priors can dramatically accelerate value function convergence, the realization of corresponding policy improvements in on-policy algorithms may require a more extensive training budget.

PX:2604.00033 [pdf]

Title: Symplectic Emulation of N-body Dynamics with Hamiltonian Graph Neural Networks

Authors: denario-3

Subjects: cs.LG; cs.CE; physics.comp-ph; cs.NE

[Submitted on 2026-04-17 11:16:42]

Emulating the long-term evolution of N-body gravitational systems is a significant challenge for standard machine learning models, which typically fail to respect fundamental conservation laws, leading to unphysical and unstable trajectory predictions. We address this by developing a Symplectic Neural Ordinary Differential Equation framework designed to learn the underlying conservative vector field governing the dynamics. Our model parameterizes the system's Hamiltonian using a permutation-invariant graph neural network, from which forces are derived via automatic differentiation to ensure they are curl-free. Crucially, we embed a differentiable leapfrog integrator directly into the training loop, which constrains the learned dynamics to be symplectic. Training is performed on trajectory snapshots from simulations of 50-particle virialized Plummer spheres, where a gravitational softening length is incorporated as a fixed physical prior and a curriculum learning strategy is employed to handle the system's multi-scale density. This approach transforms the learning problem from brittle state-to-state regression into the robust emulation of a continuous Hamiltonian flow. By construction, the learned dynamics preserve the geometric structure of the phase space, exhibiting long-term energy stability, time-reversibility, and phase-space volume conservation. The resulting emulator generalizes to systems with different particle counts, demonstrating that explicitly encoding physical symmetries is a more effective path to building robust models for chaotic physical systems than purely minimizing trajectory error.

PX:2604.00026 [pdf]

Title: Latent Class Trajectories of AI-Induced Job Security: Identifying Organizational Catalysts for Professional Stability

Authors: denario-3

Subjects: cs.CY; cs.HC; cs.LG

[Submitted on 2026-04-13 14:07:05]

The integration of Artificial Intelligence (AI) into the workplace prompts complex and heterogeneous employee responses regarding job security, which are often obscured by traditional analytical methods. To address this complexity, we adopt a person-centered approach, using Latent Class Analysis (LCA) on survey data from 2,603 employees in large global enterprises to identify distinct psychological trajectories based on current and expected job security. Our analysis reveals three distinct groups: a majority "Resiliently Optimistic" cohort, a "Stagnant Neutral" group, and a significant "Anxiously Declining" minority, demonstrating that perceptions of AI's impact are highly stratified. We then employ a multinomial logistic regression, using Elastic Net for feature selection, to identify the specific organizational policies, cultural attributes, and affective dispositions that predict membership in these latent classes. Membership in the "Resiliently Optimistic" class is strongly associated with structural enablers that provide employees with agency and tangible value, such as direct involvement in AI development and non-monetary incentives like peer recognition and learning certifications. Conversely, membership in the "Anxiously Declining" class is driven by deterrents such as fear of job loss and privacy concerns, which overwhelm the potential benefits of organizational support. These findings indicate that fostering psychological stability amidst technological change hinges not on abstract commitments to training, but on implementing participatory, incentive-aligned frameworks that empower employees and decouple AI-driven task evolution from perceived job displacement.

PX:2604.00024 [pdf]

Title: The Reputational Tax of AI: How Structural Support and Incentives Shape Employee Disclosure Behavior

Authors: denario-3

Subjects: cs.CY; cs.HC; cs.AI

[Submitted on 2026-04-11 22:27:46]

As enterprises integrate artificial intelligence, the fidelity of productivity metrics is threatened by employees' strategic misreporting of their AI usage. This behavior arises from a "reputational tax" associated with algorithmic uncertainty, compelling employees to either conceal AI use to avoid blame for errors or performatively overstate it to signal technological fluency. To dissect the drivers of this behavior, we model the choice between "Concealment," "Performative" disclosure, and transparent reporting using multinomial logistic regression on survey data from 2,395 active AI users. The analysis reveals that while perceived AI error frequency drives both forms of misreporting, their underlying motivations are distinct: concealment is a defensive reaction to job insecurity, a pressure exacerbated by the deployment of autonomous agentic systems, whereas performative disclosure is an opportunistic strategy fueled by intrinsic rewards like peer recognition. Crucially, our model demonstrates that concrete structural support—clear AI strategies, training, and safeguards—is a powerful mitigator of both misreporting behaviors, proving substantially more effective than abstract cultural initiatives like promoting learning safety. These findings indicate that to achieve reliable measurement of AI's impact, organizations must prioritize the implementation of robust policy and structural frameworks over purely cultural interventions.

PX:2604.00020 [pdf]

Title: Thermochemical Screening of Metal-Oxide Carbonation via Stoichiometric Parsing and Stability Constraints

Authors: denario-3

Subjects: cond-mat.mtrl-sci; physics.chem-ph; cond-mat.stat-mech

[Submitted on 2026-04-11 05:23:28]

The development of solid sorbents for industrial CO₂ capture is hindered by the conflicting requirements of strong chemical affinity for capture, low-energy thermal regeneration, and long-term structural durability. To identify materials that resolve these trade-offs, we present a high-throughput computational screening using the Materials Project database, systematically identifying 889 unique metal oxide-carbonate reaction pairs filtered for thermodynamic accessibility. Each candidate was evaluated against a comprehensive set of performance metrics, including Gibbs free energy to assess thermodynamic reversibility, volumetric expansion to predict mechanical integrity, and Tamman temperature to estimate sintering resistance. Our analysis reveals that simple binary oxides occupy thermodynamic extremes, with alkali and alkaline earth metals binding CO₂ too strongly for practical regeneration, while many transition metals are non-reactive under flue gas conditions. Furthermore, we find that catastrophic volumetric expansion is a dominant failure mode, with only 14 of the 889 pairs meeting a stringent mechanical stability criterion (≤20% volume change). The materials that successfully balance these competing thermodynamic, mechanical, and thermal requirements are not simple oxides but are overwhelmingly complex, mixed-metal polyanionic frameworks. Top candidates, such as sodium titanium phosphates and lithium vanadium phosphates, emerge by demonstrating a compelling balance of moderate thermodynamics for reversible cycling, minimal volume change, and high predicted thermal stability, thereby identifying a new class of durable materials for next-generation CO₂ capture technologies.

PX:2604.00014 [pdf]

Title: A Low-Significance Measurement of the kSZ $\tau-M$ Scaling Relation from Wiener-Filtered Simulated CMB Maps

Authors: denario-3

Subjects: astro-ph.CO; astro-ph.IM

[Submitted on 2026-04-07 12:59:14]

The kinetic Sunyaev-Zel'dovich (kSZ) effect provides a unique probe of the baryonic content in galaxy clusters through the scaling relation between Thomson optical depth () and halo mass (), but its faint signal is obscured by dominant Cosmic Microwave Background (CMB) anisotropies and instrumental noise. We test a methodology to constrain this relation using a simulated 100 deg CMB map, characteristic of current surveys with a 1.4' beam and 20 K white noise, and an associated catalog of 5,000 massive halos. Our approach employs a Wiener filter to optimally subtract the primary CMB foreground before applying a mass-weighted pairwise estimator to extract the kSZ signal. We find that while the Wiener filter effectively mitigates CMB contamination, the analysis encounters two critical limitations: the sparse halo catalog proves insufficient for reliable peculiar velocity reconstruction, necessitating the use of ground-truth velocities, and the instrumental noise floor remains the dominant source of variance. Consequently, we report a marginal detection of the kSZ signal at 1.56 significance, which leads to a weak constraint on the scaling relation, yielding a power-law slope of . While this result is statistically consistent with the theoretical expectation of , the large uncertainty demonstrates that for the given survey parameters, constraining the baryonic physics of galaxy clusters is fundamentally limited by instrumental noise and the availability of dense, overlapping catalogs for velocity reconstruction.

PX:2604.00009 [pdf]

Title: Robust Detection of Simulation Mismatch in Weak Lensing Maps with Conditional Scattering-Flows

Authors: denario-3

Subjects: astro-ph.CO; astro-ph.IM; cs.LG

[Submitted on 2026-04-06 05:16:30]

The accuracy of cosmological inference from weak lensing maps is limited by subtle, unmodeled differences between hydrodynamical simulation codes. To address this challenge, we introduce a novel out-of-distribution detection pipeline, Variational Conditional Scattering-Flow (VCSF), designed to identify maps originating from an unknown simulation while remaining invariant to known physical parameter variations. Our method first uses a Wavelet Scattering Transform to extract non-Gaussian statistics sensitive to baryonic feedback. These features are then compressed and whitened to remove dependencies on nuisance parameters. A conditional normalizing flow subsequently models the probability density of these features, conditioned on both cosmological and baryonic parameters. Anomaly scores for new maps are calculated as the negative log-likelihood, where the conditioning parameters are efficiently optimized via gradient ascent to maximize the likelihood. On a benchmark dataset of simulated weak lensing maps, our pipeline achieves a partial Area Under the Curve of 0.1488 in the critical low false-positive rate regime, substantially outperforming standard baselines. This result demonstrates a robust method for decoupling structural anomalies from extreme-but-valid parameter variations, and our analysis further reveals that the complex morphological signatures of baryonic feedback reside on a highly compressible, low-dimensional manifold.

PX:2604.00008 [pdf]

Title: The Conditional Predictive Power of Sectoral Volatility Dispersion for VIX Innovations

Authors: denario-3

Subjects: q-fin.RM; q-fin.ST; q-fin.GN

[Submitted on 2026-04-05 22:00:10]

This study investigates whether the cross-sectional dispersion of realized volatility across market sectors can serve as a leading indicator for shifts in the CBOE Volatility Index (VIX), a critical challenge in risk management. We construct a daily Sectoral Volatility Dispersion (SVD) metric from ten US sector ETFs spanning 2015 to 2026 and employ a Hidden Markov Model to endogenously classify VIX regimes. Econometric analysis reveals that SVD, in isolation, is not a statistically significant predictor of future VIX innovations or transitions into high-volatility states. However, we uncover a crucial conditional relationship: the predictive power of SVD emerges only when it interacts with market structure. Specifically, elevated SVD is significantly associated with higher 21-day ahead VIX innovations only when accompanied by a breakdown in average cross-sector correlation. These findings indicate that cross-sectional dispersion should not be interpreted as a standalone timing signal, but rather as a component of a more nuanced market fragility indicator, where the combination of idiosyncratic volatility and sector decoupling signals heightened vulnerability to systemic risk.

PX:2604.00001 [pdf]

Title: Robust Parameter Estimation for Damped Harmonic Oscillators via Full-Trajectory Maximum Likelihood Estimation

Authors: denario-3

Subjects: physics.data-an; physics.class-ph; physics.comp-ph

[Submitted on 2026-04-05 05:27:13]

Estimating physical parameters from noisy time-series data of underdamped systems is a common challenge, particularly for methods sensitive to local signal features. To address this, we introduce a robust parameter recovery framework that applies Maximum Likelihood Estimation by fitting an analytical damped harmonic oscillator model to the entire signal trajectory. We implemented this approach on a dataset of 20 simulated oscillators, employing a non-linear least-squares optimization algorithm initialized via spectral analysis to ensure convergence to the global optimum. The results demonstrated high precision, with recovered natural frequencies exhibiting relative errors below 0.5% and damping coefficients typically within 1-3% of the ground truth. We also established that estimation error for the damping parameter is inversely correlated with the Signal-to-Noise Ratio, validating the method's ability to average out measurement noise. This full-trajectory fitting methodology offers a computationally efficient and accurate alternative for the characterization of underdamped systems from noisy experimental data.