Summary of Prose-fd: a Multimodal Pde Foundation Model For Learning Multiple Operators For Forecasting Fluid Dynamics, by Yuxuan Liu et al.
PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics
by Yuxuan Liu, Jingmin Sun, Xinjie He, Griffin Pinney, Zecheng Zhang, Hayden Schaeffer
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA); Fluid Dynamics (physics.flu-dyn)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes PROSE-FD, a novel zero-shot multimodal PDE foundational model for predicting heterogeneous two-dimensional physical systems related to fluid dynamics settings, including shallow water and Navier-Stokes equations. The transformer-based multi-operator learning approach incorporates symbolic information to perform non-autoregressive operator-based data prediction. By fusing multiple modalities in the inputs, the model includes mathematical descriptions of physical behavior. The foundation model is pre-trained on 6 parametric families of equations from 13 datasets, including over 60K trajectories. PROSE-FD outperforms popular operator learning, computer vision, and multi-physics models in benchmark forward prediction tasks. Ablation studies test architecture choices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to predict how different physical systems work together, like water flowing through pipes or air moving over planes. They call this model PROSE-FD. It uses special math and computer science tricks to understand how these systems behave without needing any training data. The team tested their model on many examples and found it works better than other methods in predicting what will happen next. |
Keywords
» Artificial intelligence » Autoregressive » Transformer » Zero shot
