11 papers
Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems
Yaozhong Shi, Zachary E. Ross, Yisong Yue
Principled regression for stochastic processes is a long-standing challenge with deep connections to scientific inverse problems. We introduce Flow Annealing Posterior Sampling (FA…
Enforcing Reciprocity in Operator Learning for Seismic Wave Propagation
Caifeng Zou, Yaozhong Shi, Zachary E. Ross +2
Accurate and efficient wavefield modeling underpins seismic structure and source studies. Traditional methods comply with physical laws but are computationally intensive. Data-driv…
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…
Forward and Inverse Mantle Convection with Neural Operators
Chenxi Kong, Michael Gurnis, Zachary E. Ross
Thermal state reconstruction--reversing convection to recover the thermal structure of the mantle at an earlier geologic time--is an important tool to understand the evolution of m…
Ambient Noise Full Waveform Inversion with Neural Operators
Caifeng Zou, Zachary E. Ross, Robert W. Clayton +2
Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finit…
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…