1.1k citations · 2.4k across the 34 of their papers we have counts for
38 papers
Scale-Split Neural Operator for Memory- and Data-Efficient 3D Turbulence Prediction
Shaoxiang Qin, Yucheng Zhao, Zongyi Li +2
Neural surrogates have emerged as fast alternatives to the numerical simulation of three-dimensional turbulence. However, training them at high resolution remains challenging, sinc…
GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
Haixu Wu, Minghao Guo, Zongyi Li +4
Neural simulators promise efficient surrogates for physics simulation, but scaling them is bottlenecked by the prohibitive cost of generating high-fidelity training data. Pre-train…
Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows
Zelin Zhao, Zongyi Li, Kimia Hassibi +5
Assessing turbulence control effects for wall friction numerically is a significant challenge since it requires expensive simulations of turbulent fluid dynamics. We instead propos…
Physics-Aware Neural Operators for Direct Inversion in 3D Photoacoustic Tomography
Jiayun Wang, Yousuf Aborahama, Arya Khokhar +10
Learning physics-constrained inverse operators-rather than post-processing physics-based reconstructions-is a broadly applicable strategy for problems with expensive forward models…
Geometric Operator Learning with Optimal Transport
Xinyi Li, Zongyi Li, Nikola Kovachki +1
We propose integrating optimal transport (OT) into operator learning for partial differential equations (PDEs) on complex geometries. Classical geometric learning methods typically…
Scale-Consistent Learning for Partial Differential Equations
Zongyi Li, Samuel Lanthaler, Catherine Deng +4
Machine learning (ML) models have emerged as a promising approach for solving partial differential equations (PDEs) in science and engineering. Previous ML models typically cannot…
High precision PINNs in unbounded domains: application to singularity formulation in PDEs
Yixuan Wang, Ziming Liu, Zongyi Li +2
We investigate the high-precision training of Physics-Informed Neural Networks (PINNs) in unbounded domains, with a special focus on applications to singularity formulation in PDEs…
Ultrasound Lung Aeration Map via Physics-Aware Neural Operators
Jiayun Wang, Oleksii Ostras, Masashi Sode +5
Lung ultrasound is a growing modality in clinics for diagnosing and monitoring acute and chronic lung diseases due to its low cost and accessibility. Lung ultrasound works by emitt…
A Library for Learning Neural Operators
Jean Kossaifi, Nikola Kovachki, Zongyi Li +8
We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimens…
Coarse Graining with Neural Operators for Simulating Chaotic Systems
Chuwei Wang, Boris Bonev, Julius Berner +6
Accurately predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling. However, achieving such predictions typically requires…
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy +9
Existing neural operator architectures face challenges when solving multiphysics problems with coupled partial differential equations (PDEs) due to complex geometries, interactions…
Plasma Surrogate Modelling using Fourier Neural Operators
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi +10
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma rap…
Neural Operators for Accelerating Scientific Simulations and Design
Kamyar Azizzadenesheli, Nikola Kovachki, Zongyi Li +3
Scientific discovery and engineering design are currently limited by the time and cost of physical experiments, selected mostly through trial-and-error and intuition that require d…
Geometry-Informed Neural Operator for Large-Scale 3D PDEs
Zongyi Li, Nikola Borislavov Kovachki, Chris Choy +8
We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the solution operator of large-scale partial differential equations with varying ge…
AI-aided Geometric Design of Anti-infection Catheters
Tingtao Zhou, Xuan Wan, Daniel Zhengyu Huang +6
Bacteria can swim upstream due to hydrodynamic interactions with the fluid flow in a narrow tube, and pose a clinical threat of urinary tract infection to patients implanted with c…
Nonlocality and Nonlinearity Implies Universality in Operator Learning
Samuel Lanthaler, Zongyi Li, Andrew M. Stuart
Neural operator architectures approximate operators between infinite-dimensional Banach spaces of functions. They are gaining increased attention in computational science and engin…
Fourier Neural Operator for Plasma Modelling
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi +3
Predicting plasma evolution within a Tokamak is crucial to building a sustainable fusion reactor. Whether in the simulation space or within the experimental domain, the capability…
Forecasting subcritical cylinder wakes with Fourier Neural Operators
Peter I Renn, Cong Wang, Sahin Lale +3
We apply Fourier neural operators (FNOs), a state-of-the-art operator learning technique, to forecast the temporal evolution of experimentally measured velocity fields. FNOs are a…
Machine Learning Accelerated PDE Backstepping Observers
Yuanyuan Shi, Zongyi Li, Huan Yu +3
State estimation is important for a variety of tasks, from forecasting to substituting for unmeasured states in feedback controllers. Performing real-time state estimation for PDEs…
FC-PINO: High Precision Physics-Informed Neural Operators via Fourier Continuation
Adarsh Ganeshram, Haydn Maust, Valentin Duruisseaux +6
The physics-informed neural operator (PINO) is a machine learning paradigm that has demonstrated promising results for learning solutions to partial differential equations (PDEs).…
Incremental Spatial and Spectral Learning of Neural Operators for Solving Large-Scale PDEs
Robert Joseph George, Jiawei Zhao, Jean Kossaifi +2
Fourier Neural Operators (FNO) offer a principled approach to solving challenging partial differential equations (PDE) such as turbulent flows. At the core of FNO is a spectral lay…
An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design
Mingjie Liu, Haoyu Yang, Zongyi Li +7
Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. It requires rigorous simulations of optical and chemical models that are co…
Real-time high-resolution CO geological storage prediction using nested Fourier neural operators
Gege Wen, Zongyi Li, Qirui Long +3
Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoi…
Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training
Haoyu Yang, Zongyi Li, Kumara Sastry +5
Machine learning techniques have been extensively studied for mask optimization problems, aiming at better mask printability, shorter turnaround time, better mask manufacturability…