Search Results for author:
Found 12 papers, 6 papers with code
Universal Functional Regression with Neural Operator Flows
We empirically study the performance of OpFlow on regression and generation tasks with data generated from Gaussian processes with known posterior forms and non-Gaussian processes, as well as real-world earthquake seismograms with an unknown closed-form distribution.
Broadband Ground Motion Synthesis via Generative Adversarial Neural Operators: Development and Validation
Lastly, cGM-GANO produces similar median scaling to traditional GMMs for frequencies greater than 1Hz for both PSA and EAS but underestimates the aleatory variability of EAS.
Generative Adversarial Neural Operators
The inputs to the generator are samples of functions from a user-specified probability measure, e. g., Gaussian random field (GRF), and the generator outputs are synthetic data functions.
U-NO: U-shaped Neural Operators
We show that U-NO results in an average of 26% and 44% prediction improvement on Darcy's flow and turbulent Navier-Stokes equations, respectively, over the state of the art.
Seismic wave propagation and inversion with Neural Operators
We develop a scheme to train Neural Operators on an ensemble of simulations performed with random velocity models and source locations.
Deep Learning-based Damage Mapping with InSAR Coherence Time Series
In this study, we propose a novel approach to damage mapping, combining deep learning with the full time history of SAR observations of an impacted region in order to detect anomalous variations in the Earth's surface properties due to a natural disaster.
HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks
We introduce a scheme for probabilistic hypocenter inversion with Stein variational inference.
Data-driven Accelerogram Synthesis using Deep Generative Models
Our approach extends the Wasserstein GAN formulation to allow for the generation of ground-motions conditioned on a set of continuous physical variables.
EikoNet: Solving the Eikonal equation with Deep Neural Networks
Here, we propose EikoNet, a deep learning approach to solving the Eikonal equation, which characterizes the first-arrival-time field in heterogeneous 3D velocity structures.
Extracting dispersion curves from ambient noise correlations using deep learning
We present a machine-learning approach to classifying the phases of surface wave dispersion curves.
Directivity Modes of Earthquake Populations with Unsupervised Learning
A seismic spectral decomposition technique is used to first produce relative measurements of radiated energy for earthquakes in a spatially-compact cluster.
PhaseLink: A Deep Learning Approach to Seismic Phase Association
For the examined datasets, PhaseLink can precisely associate P- and S-picks to events that are separated by ~12 seconds in origin time.
