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Foundation models accelerate inference for differential equations

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a suite of Foundation Inference Models (FIMs) designed to rapidly estimate parameters for various differential equations from time-series data. These models, including FIM-SDE for stochastic differential equations, FIM-PP for temporal point processes, and FIM-ODE for ordinary differential equations, are pretrained on broad distributions of synthetic data. This pretraining allows them to perform in-context (zero-shot) inference or be quickly fine-tuned to specific datasets, often outperforming traditional methods and specialized models that require extensive training. AI

IMPACT These foundation models could significantly speed up scientific discovery by enabling faster and more accurate parameter estimation for complex dynamical systems.

RANK_REASON The cluster contains multiple research papers detailing new models and methods for scientific inference.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Patrick Seifner, Kostadin Cvejoski, David Berghaus, Cesar Ojeda, Ramses J. Sanchez ·

    In-Context Learning of Stochastic Differential Equations with Foundation Inference Models

    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…

  2. arXiv cs.LG TIER_1 English(EN) · David Berghaus, Patrick Seifner, Kostadin Cvejoski, C\'esar Ojeda, Rams\'es J. S\'anchez ·

    In-Context Learning of Temporal Point Processes with Foundation Inference Models

    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…

  3. arXiv cs.LG TIER_1 English(EN) · Maximilian Mauel, Johannes R. H\"ubers, David Berghaus, Patrick Seifner, Ramses J. Sanchez ·

    Foundation Inference Models for Ordinary Differential Equations

    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…

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