发表机构
Tel Aviv University（特拉维夫大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究预训练生成式图像编辑模型能否为数值模拟提供通用接口，通过既定求解器监督，将相同架构和协议应用于多种方程，结果显示该模型可表示多样物理映射，还识别了相关基本约束。
AI 中文摘要
我们研究预训练的生成式图像编辑模型是否能为数值模拟提供通用接口。物理输入和解决方案呈现为图像，标量如材料属性、扩散率和加载参数通过轻量级适配器输入。利用既定数值和解析求解器进行监督，我们将相同架构和训练协议应用于异质椭圆方程、强迫热与伯格斯演化、复金兹堡 - 朗道动力学、二维纳维 - 斯托克斯预测、势流、弹性、程函旅行时间、相场断裂和熵最优传输。结果表明，当每个任务通过合适的视觉编码表达时，预训练图像模型可表示各种静态和时间相关的物理映射，包括不稳定和类似冲击的行为。这项工作是能力研究而非超越专用求解器的尝试，还识别了基本约束：图像和潜在表示使数值范围选择及控制方程或不变量的直接执行复杂化，而失败的Kuramoto - Sivashinsky实验表明表示误差阻碍了混沌系统有意义的长期模拟。
英文摘要
We investigate whether a pretrained generative image-editing model can provide a common interface for numerical simulation. Physical inputs and solutions are rendered as images, while scalar quantities such as material properties, diffusivity, and loading parameters enter through lightweight adapters. Using established numerical and analytic solvers for supervision, we apply the same architecture and training protocol to heterogeneous elliptic equations, forced heat and Burgers evolution, complex Ginzburg-Landau dynamics, two-dimensional Navier-Stokes prediction, potential flow, elasticity, eikonal travel time, phase-field fracture, and entropic optimal transport. The results show that a pretrained image model can represent diverse static and time-dependent physical mappings, including unstable and shock-like behavior, when each task is expressed through a suitable visual encoding. This work is a capability study rather than an attempt to surpass specialized solvers. It also identifies fundamental constraints: image and latent representations complicate numerical range selection and direct enforcement of governing equations or invariants, while a failed Kuramoto-Sivashinsky experiment indicates that representation errors prevent meaningful long-horizon simulation of chaotic systems.
