33 citations · 40 across the 12 of their papers we have counts for
13 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 (FL…
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…
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…
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…
A Schauder Basis for Multiparameter Persistence
Peter Bubenik, Zachariah Ross
Certain classes of multiparameter persistence modules may be encoded as signed barcodes, represented as points in a polyhedral subset of Euclidean space, we refer to as signed pers…
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…
Reducing Frequency Bias of Fourier Neural Operators in 3D Seismic Wavefield Simulations Through Multi-Stage Training
Qingkai Kong, Caifeng Zou, Youngsoo Choi +5
The recent development of Neural Operator (NeurOp) learning for solutions to the elastic wave equation shows promising results and provides the basis for fast large-scale simulatio…
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…
InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences
Hongkai Zheng, Wenda Chu, Bingliang Zhang +9
Plug-and-play diffusion priors (PnPDP) have emerged as a promising research direction for solving inverse problems. However, current studies primarily focus on natural image restor…
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…
Universal Functional Regression with Neural Operator Flows
Yaozhong Shi, Angela F. Gao, Zachary E. Ross +1
Regression on function spaces is typically limited to models with Gaussian process priors. We introduce the notion of universal functional regression, in which we aim to learn a pr…
Deep Neural Helmholtz Operators for 3D Elastic Wave Propagation and Inversion
Caifeng Zou, Kamyar Azizzadenesheli, Zachary E. Ross +1
Numerical simulations of seismic wave propagation in heterogeneous 3D media are central to investigating subsurface structures and understanding earthquake processes, yet are compu…
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…