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English(EN) PDEFlow: Autonomous Agentic PDE Pipelines for Neural Operator Learning and Solver-Free Inference

新框架自动化微分方程的神经算子流水线

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出PDEFlow，一个自主智能体式框架，旨在自动化求解微分方程的神经算子流水线的创建。该系统将用户级别的ODE和PDE描述转换为可执行的流水线，处理问题规范、使用FEniCSx后端进行数据生成以及神经算子训练和推理。该框架旨在通过最大限度地减少复杂模拟和学习任务中的手动干预来简化科学和工程工作流程。 AI

影响 通过自动化复杂的模拟和学习任务来简化科学和工程工作流程。

排序理由 该集群包含一篇详细介绍科学计算新框架的研究论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新框架自动化微分方程的神经算子流水线

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Akshat Jani, Prathamesh Gadekar, Sakhinana Sagar Srinivas, Venkataramana Runkana ·

    PDEFlow：用于神经算子学习和无求解器推理的自主智能体PDE管道

    arXiv:2607.05134v1 Announce Type: cross Abstract: We present PDEFlow, an autonomous agentic framework that turns user-level ODE and PDE descriptions into solver-backed neural-operator pipelines. The workflow links problem specification, data generation, operator training, and che…

  2. arXiv cs.AI TIER_1 English(EN) · Venkataramana Runkana ·

    PDEFlow：用于神经算子学习和无求解器推理的自主智能体PDE管道

    We present PDEFlow, an autonomous agentic framework that turns user-level ODE and PDE descriptions into solver-backed neural-operator pipelines. The workflow links problem specification, data generation, operator training, and checkpoint-based inference. A stateful input graph co…

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