arXiv subjects

Jinyan Zhao

Publications and source records attributed to Jinyan Zhao.

3 recordsLinked to original sources

arXiv2026

Scalable Gaussian Process for Learning Non-Ergodic Ground Motion Model from Physics-Based Simulations with Application to Power Infrastructure Assessment

This study presents the development and application of a scalable non-ergodic ground motion model (NGMM) for the Los Angeles area. The NGMM is trained and validated on physics-based simulated ground-motion data from a recent Statewide California Earthquake Center (SCEC) CyberShake study. The NGMM is formulated as a Gaussian Process (GP) regression model, where the prior median is defined as the ASK14 ergodic ground-motion model and the posterior median is obtained by learning the non-ergodic effects embedded in the training data. These non-ergodic effects include systematic site and path effects, which are represented in the GP using Mat\'ern and specialized covariance kernels that explicitly characterize path vectors. Implementing the NGMM requires hyperparameter tuning and inference on large datasets (on the order of one million data points or more), posing significant computational challenges for conventional GP approaches. To address this scalability issue, this paper presents a suite of computational strategies, including sparse Cholesky inversion, parallel computing, GPU acceleration, and stochastic gradient descent minimization. Despite these advances, the full CyberShake dataset (on the order of hundreds of millions of data points) remains computationally prohibitive. Therefore, aleatory variability is modeled separately using a mixed-effects formulation to represent within-event and between-event variability. The developed NGMM has two primary applications: interpolation of partially observed ground-motion fields and predictive modeling for ground motions in unobserved earthquake scenarios. Validation results on independent datasets demonstrate accurate performance in both applications. A case study of power transmission network assessment in an Mw 6.7 Puente Hill scenario further demonstrated that the developed NGMM closely reproduces physics-based simulation results.

stat.AP↗

arXiv2026

Phase Transitions in Collective Damage of Civil Structures under Natural Hazards

The fate of cities under natural hazards depends not only on hazard intensity but also on the coupling of structural damage, a collective process that remains poorly understood. Here we show that urban structural damage exhibits phase-transition phenomena. As hazard intensity increases, the system can shift abruptly from a largely safe to a largely damaged state, analogous to a first-order phase transition in statistical physics. Higher diversity in the building portfolio smooths this transition, but multiscale damage clustering traps the system in an extended critical-like regime, analogous to a Griffiths phase, suppressing the emergence of a more predictable disordered (Gaussian) phase. These phenomenological patterns are interpreted through an effective random-field Ising model, with the external field, disorder strength, and temperature interpreted as the effective hazard demand, structural diversity, and modeling uncertainty, respectively. Applying this framework to real urban inventories reveals that widely used engineering modeling practices can shift urban damage patterns between synchronized and volatile regimes, systematically biasing exceedance-based risk metrics by up to 50% under moderate earthquakes ($M_w \approx 5.5$-$6.0$), equivalent to a several-fold gap in repair costs. This phase-aware description turns the collective behavior of civil infrastructure damage into actionable diagnostics for urban risk assessment and planning.

stat.AP↗

arXiv2025

Characterising injection signatures in Jupiter's ultraviolet aurora using Juno observations

Discrete features in Jupiter's ultraviolet aurora have been interpreted as signatures of plasma injections in the middle magnetosphere. There exists some ambiguity whether magnetodisc scattering or high-latitude Alfvenic acceleration best describes the observed properties of these injection signatures, and also to what extent arcs in the outer emission are related to injections. Many injection signatures are the result of the evolution of dawn storms; there is, however, limited evidence that non-dawn-storm injection signatures are sometimes present in the aurora. We use automatic detection of these discrete features, alongside data from Juno-UVS and in-situ measurements by other Juno instruments, to show that scattering likely accounts for most of the electron precipitation associated with injection signatures. Additionally, there is evidence that injection signatures can be classified into two types: dawn-storm and non-dawn-storm. Arc-like features in the outer emission show very similar properties to traditional blob-like injection signatures and may consist of sequences of injection signatures that have broadened into an arc via energy-dependent electron drift.

astro-ph.EP↗