8 papers
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…
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…
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…
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…
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…