23 citations · 40 across the 10 of their papers we have counts for
13 papers
Cross-Domain Pretraining for Steady-State Neural CFD Surrogates
Anthony Zhou, Amir Barati Farimani, Shirley Ho +1
Neural surrogates for computational fluid dynamics (CFD) have the potential to greatly enhance engineering innovation through accelerating simulation. However, the primary limitati…
Generative Neural Operators through Diffusion Last Layer
Sungwon Park, Anthony Zhou, Hongjoong Kim +1
Neural operators provide a powerful framework for learning discretization invariant mappings between function spaces, but standard deterministic models do not capture predictive un…
Reframing Generative Models for Physical Systems using Stochastic Interpolants
Anthony Zhou, Alexander Wikner, Amaury Lancelin +2
Generative models have recently emerged as powerful surrogates for physical systems, demonstrating increased accuracy, stability, and/or statistical fidelity. Most approaches rely…
BlastOFormer: Attention and Neural Operator Deep Learning Methods for Explosive Blast Prediction
Reid Graves, Anthony Zhou, Amir Barati Farimani
Accurate prediction of blast pressure fields is essential for applications in structural safety, defense planning, and hazard mitigation. Traditional methods such as empirical mode…
Hamiltonian Neural PDE Solvers through Functional Approximation
Anthony Zhou, Amir Barati Farimani
Designing neural networks within a Hamiltonian framework offers a principled way to ensure that conservation laws are respected in physical systems. While promising, these capabili…
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…
Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates
Anthony Zhou, Amir Barati Farimani
Neural surrogates for partial differential equations (PDEs) have become popular due to their potential to quickly simulate physics. With a few exceptions, neural surrogates general…
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…
Strategies for Pretraining Neural Operators
Anthony Zhou, Cooper Lorsung, AmirPouya Hemmasian +1
Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance. Des…
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…
Proof-of-concept: Using ChatGPT to Translate and Modernize an Earth System Model from Fortran to Python/JAX
Anthony Zhou, Linnia Hawkins, Pierre Gentine
Earth system models (ESMs) are vital for understanding past, present, and future climate, but they suffer from legacy technical infrastructure. ESMs are primarily implemented in Fo…
Masked Autoencoders are PDE Learners
Anthony Zhou, Amir Barati Farimani
Neural solvers for partial differential equations (PDEs) have great potential to generate fast and accurate physics solutions, yet their practicality is currently limited by their…
FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification
Anthony Zhou, Amir Barati Farimani
The growth of global consumption has motivated important applications of deep learning to smart manufacturing and machine health monitoring. In particular, analyzing vibration data…