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 (or discovery) of these functions from data is a central problem in machine learning, with wide application across the natural and social sciences. Yet current solutions either rely heavily on prior knowledge of the dynamics or involve intricate training procedures. We introduce FIM-SDE (Foundation Inference Model for SD
The proliferation of advanced AI models and the increasing availability of complex real-world data are driving innovation in methods for understanding dynamic systems.
Improved models for stochastic differential equations (SDEs) will enable more accurate predictions and control in diverse scientific and engineering fields, from finance to climate modeling.
The ability to infer SDEs with less prior knowledge and intricate training simplifies a complex machine learning problem, making advanced dynamic system modeling more accessible and efficient.
- · AI/ML researchers
- · Quantitative finance
- · Climate modeling
- · Drug discovery
- · Traditional SDE modeling approaches
- · Industries reliant on less accurate predictive models
More robust and generalizable AI models capable of handling uncertainty and complex time-series data.
Accelerated discovery in scientific domains due to AI's enhanced ability to model chaotic or unpredictable systems.
New classes of autonomous AI agents operating effectively in highly dynamic and uncertain real-world environments.
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Read at arXiv cs.LG