arXiv:2604.02121v2 Announce Type: replace-cross Abstract: Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging. For deterministic models, parameter inference often relies on gradients, which can be obtained efficiently through automatic differentiation (AD). However, AD cannot be applied directly to the Gillespie stochastic simulation algorithm (SSA), since sampling from a discrete set of reactions introduces non-differentiable operations. In this work, we adopt three gradient estimators from machine learning for the Gil
This work is published as part of ongoing academic research in AI and scientific computing, addressing a known challenge in parameter inference for complex physical models.
Improved gradient estimators enhance the ability to model and understand complex stochastic physical and biological systems, accelerating scientific discovery and engineering applications.
The ability to more efficiently infer parameters in discrete stochastic kinetic models becomes more robust and accessible, potentially bridging a gap between deterministic and stochastic modeling approaches with AI techniques.
- · Computational physicists
- · Synthetic biologists
- · Machine learning researchers
- · Pharmaceutical R&D
- · Traditional stochastic simulation approaches
More accurate and faster parameter inference for stochastic kinetic models becomes possible across various scientific domains.
Accelerated development of new materials, drugs, and biological systems due to better predictive modeling capabilities.
Enhanced ability to engineer novel synthetic biology pathways and complex chemical processes with greater precision and speed.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG