arXiv subjects

Adva Baratz

Publications and source records attributed to Adva Baratz.

3 recordsLinked to original sources

arXiv2026

A Cloud Condensation Model on Insoluble Nuclei Validated with Radiosonde and Ceilometer Data: Implications for Dust based Rainfall Forecasting

This study introduces and validates a cloud condensation model on insoluble nuclei using a novel comparative analysis of simultaneous radiosonde and ceilometer data. A transformation of the radiosonde's temperature and relative humidity profiles into a simulated optical backscatter signal is implemented via the model, which includes a critical rate-limiting activation step. By comparing this simulated signal with the ceilometer's measured data, the model can determine the minimum effective size of dust particles required to act as cloud condensation nuclei. This approach has direct implications for improving the parameterization of cloud formation in local weather models, particularly for rainfall forecasting in dust-prone arid and semi-arid regions.

physics.ao-ph↗

arXiv2025

Data-Driven Reconstruction and Characterization of Stochastic Dynamics via Dynamical Mode Decomposition

Noise fundamentally limits the performance and predictive capabilities of classical and quantum dynamical systems by degrading stability and obscuring intrinsic dynamical characteristics. Characterizing such noise accurately is essential for enhancing measurement precision, understanding environmental interactions, and designing effective control strategies across diverse scientific and engineering domains. However, extracting the environment spectral features and associated characteristic decay or coherence times from limited and noisy datasets remains challenging. Here, we introduce a general, data-driven framework based on Dynamical Mode Decomposition (DMD) to analyze system dynamics under stochastic noise. We reinterpret DMD modes as statistical weights over an ensemble of stochastic trajectories and, via a nonlinear mapping, construct a PSD-like spectral fingerprint of the noise. This enables the identification of dominant frequency contributions in both broadband (white) and correlated ($1/f$) noise environments, as well as direct extraction of intrinsic characteristic decay times from DMD eigenvalues. To overcome instability in standard DMD-based extrapolation, we develop a constrained reconstruction method using extracted decay times as physical bounds and the learned noise as spectral weights. We demonstrate the effectiveness of this approach through simulations of quantum system dynamics subject to decoherence from noise, demonstrating its robustness and predictive capabilities and comparing it with standard methods. This methodology provides a trajectory-level framework for diagnostic and predictive analysis of stochastic processes from limited, noisy time-series data.

quant-ph↗

arXiv2024

Unsupervised learning approach to quantum wavepacket dynamics from coupled temporal-spatial correlations

Understanding complex quantum dynamics in realistic materials requires insight into the underlying correlations dominating the interactions between the participating particles. Due to the wealth of information involved in these processes, applying artificial intelligence methods is compelling. Yet, unsupervised data-driven approaches typically focus on maximal variations of the individual components, rather than considering the correlations between them. Here we present an approach that recognizes correlation patterns to explore convoluted dynamical processes. Our scheme is using singular value decomposition (SVD) to extract dynamical features, unveiling the internal temporal-spatial interrelations that generate the dynamical mechanisms. We apply our approach to study light-induced wavepacket propagation in organic crystals, of interest for applications in material based quantum computing and quantum information science. We show how transformation from the input momentum and time coordinates onto a new correlation-induced coordinate space allows direct recognition of the relaxation and dephasing components dominating the dynamics and demonstrate their dependence on the initial pulse shape. Entanglement of the dynamical features is suggested as a pathway to reproduce the information required for further explainability of these mechanisms. Our method offers a route for elucidating complex dynamical processes using unsupervised AI-based analysis in multi-component systems.

cond-mat.mtrl-sci↗