15 citations · 29 across the 8 of their papers we have counts for
9 papers
Scalable Gaussian Process for Learning Non-Ergodic Ground Motion Model from Physics-Based Simulations with Application to Power Infrastructure Assessment
Jinyan Zhao, Grigorios Lavrentiadis, Domniki Asimaki
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-base…
Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching
Yaozhong Shi, Grigorios Lavrentiadis, Konstantinos Tsalouchidis +5
Earthquake hazard analysis and design of spatially distributed infrastructure, such as power grids and energy pipeline networks, require scenario-specific ground-motion time histor…
Mesh-Informed Neural Operator : A Transformer Generative Approach
Yaozhong Shi, Zachary E. Ross, Domniki Asimaki +1
Generative models in function spaces, situated at the intersection of generative modeling and operator learning, are attracting increasing attention due to their immense potential…
Stochastic Process Learning via Operator Flow Matching
Yaozhong Shi, Zachary E. Ross, Domniki Asimaki +1
Expanding on neural operators, we propose a novel framework for stochastic process learning across arbitrary domains. In particular, we develop operator flow matching (OFM) for lea…
Data-driven Characterization of Near-Surface Velocity in the San Francisco Bay Area: A Stationary and Spatially Varying Approach
Grigorios Lavrentiadis, Elnaz Seylabi, Feiruo Xia +3
This study presents the development of two new sedimentary velocity models for the San Francisco Bay Area (SFBA) to improve the near-surface representation of shear-wave velocity (…
Broadband Ground Motion Synthesis via Generative Adversarial Neural Operators: Development and Validation
Yaozhong Shi, Grigorios Lavrentiadis, Domniki Asimaki +2
We present a data-driven framework for ground-motion synthesis that generates three-component acceleration time histories conditioned on moment magnitude, rupture distance , time-a…
Data-driven Accelerogram Synthesis using Deep Generative Models
Manuel A. Florez, Michaelangelo Caporale, Pakpoom Buabthong +3
Robust estimation of ground motions generated by scenario earthquakes is critical for many engineering applications. We leverage recent advances in Generative Adversarial Networks…
Site characterization at downhole arrays by joint inversion of dispersion data and acceleration time series
Elnaz Seylabi, Andrew Stuart, Domniki Asimaki
We present a sequential data assimilation algorithm based on the ensemble Kalman inversion to estimate the near-surface shear wave velocity profile and damping when heterogeneous d…
Basin Effects in Strong Ground Motion: A Case Study from the 2015 Gorkha, Nepal Earthquake
Peyman Ayoubi, Domniki Asimaki, Kami Mohammadi
The term "basin effects" refers to entrapment and reverberation of earthquake waves in soft sedimentary deposits underlain by concave basement rock structures. Basin effects can si…