研究人员开发了一套基础推理模型（FIMs），旨在从时间序列数据中快速估计各种微分方程的参数。这些模型包括用于随机微分方程的FIM-SDE、用于时间点过程的FIM-PP以及用于常微分方程的FIM-ODE，它们在广泛的合成数据分布上进行了预训练。这种预训练使它们能够执行上下文（零样本）推理，或快速微调以适应特定数据集，其性能通常优于需要大量训练的传统方法和专用模型。 AI
影响 这些基础模型通过实现对复杂动力学系统更快、更准确的参数估计，有望显著加速科学发现。
排序理由 该集群包含多篇详细介绍科学推理新模型和方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2502.19049v3 Announce Type: replace Abstract: Stochastic differential equations (SDEs) describe dynamical systems where deterministic flows, governed by a drift function, are superimposed with random fluctuations, dictated by a diffusion function. The accurate estimation (o…
arXiv:2509.24762v3 Announce Type: replace Abstract: Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future events. Current neural network approaches to MTPP in…
arXiv:2602.08733v2 Announce Type: replace Abstract: Ordinary differential equations (ODEs) are central to scientific modelling, but inferring their vector fields from noisy trajectories remains challenging. Current approaches such as symbolic regression, Gaussian process (GP) reg…