206 citations · 447 across the 15 of their papers we have counts for
16 papers
MMCORE: MultiModal COnnection with Representation Aligned Latent Embeddings
Zijie Li, Yichun Shi, Jingxiang Sun +8
We present MMCORE, a unified framework designed for multimodal image generation and editing. MMCORE leverages a pre-trained Vision-Language Model (VLM) to predict semantic visual e…
Generative Latent Neural PDE Solver using Flow Matching
Zijie Li, Anthony Zhou, Amir Barati Farimani
Autoregressive next-step prediction models have become the de-facto standard for building data-driven neural solvers to forecast time-dependent partial differential equations (PDEs…
Dual Diffusion for Unified Image Generation and Understanding
Zijie Li, Henry Li, Yichun Shi +4
Diffusion models have gained tremendous success in text-to-image generation, yet still lag behind with visual understanding tasks, an area dominated by autoregressive vision-langua…
Text2PDE: Latent Diffusion Models for Accessible Physics Simulation
Anthony Zhou, Zijie Li, Michael Schneier +2
Recent advances in deep learning have inspired numerous works on data-driven solutions to partial differential equation (PDE) problems. These neural PDE solvers can often be much f…
CaFA: Global Weather Forecasting with Factorized Attention on Sphere
Zijie Li, Anthony Zhou, Saurabh Patil +1
Accurate weather forecasting is crucial in various sectors, impacting decision-making processes and societal events. Data-driven approaches based on machine learning models have re…
Inpainting Computational Fluid Dynamics with Deep Learning
Dule Shu, Wilson Zhen, Zijie Li +1
Fluid data completion is a research problem with high potential benefit for both experimental and computational fluid dynamics. An effective fluid data completion method reduces th…
Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
Zijie Li, Saurabh Patil, Francis Ogoke +5
Neural networks have shown promising potential in accelerating the numerical simulation of systems governed by partial differential equations (PDEs). Different from many existing n…
Pretraining Strategy for Neural Potentials
Zehua Zhang, Zijie Li, Amir Barati Farimani
We propose a mask pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water systems. GNNs are pre…
Hyena Neural Operator for Partial Differential Equations
Saurabh Patil, Zijie Li, Amir Barati Farimani
Numerically solving partial differential equations typically requires fine discretization to resolve necessary spatiotemporal scales, which can be computationally expensive. Recent…
Scalable Transformer for PDE Surrogate Modeling
Zijie Li, Dule Shu, Amir Barati Farimani
Transformer has shown state-of-the-art performance on various applications and has recently emerged as a promising tool for surrogate modeling of partial differential equations (PD…
Physics Informed Token Transformer for Solving Partial Differential Equations
Cooper Lorsung, Zijie Li, Amir Barati Farimani
Solving Partial Differential Equations (PDEs) is the core of many fields of science and engineering. While classical approaches are often prohibitively slow, machine learning model…
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials
Yuyang Wang, Changwen Xu, Zijie Li +1
Recent advances in equivariant graph neural networks (GNNs) have made deep learning amenable to developing fast surrogate models to expensive ab initio quantum mechanics (QM) appro…
A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction
Dule Shu, Zijie Li, Amir Barati Farimani
Machine learning models are gaining increasing popularity in the domain of fluid dynamics for their potential to accelerate the production of high-fidelity computational fluid dyna…
Graph Neural Networks for Molecules
Yuyang Wang, Zijie Li, Amir Barati Farimani
Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems. This review introduces GNNs…
Transformer for Partial Differential Equations' Operator Learning
Zijie Li, Kazem Meidani, Amir Barati Farimani
Data-driven learning of partial differential equations' solution operators has recently emerged as a promising paradigm for approximating the underlying solutions. The solution ope…
Graph Neural Networks Accelerated Molecular Dynamics
Zijie Li, Kazem Meidani, Prakarsh Yadav +1
Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter. Since the resolution of MD is atomic-scale, achieving long time-scale…