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[2602.19775] Exact Discrete Stochastic Simulation with Deep-Learning-Scale Gradient Optimization
Summary
The paper presents a novel approach to exact discrete stochastic simulation using deep-learning-scale gradient optimization, enhancing scalability and accuracy in various scientific domains.
Why It Matters
This research is significant as it addresses the limitations of existing stochastic simulation methods by enabling gradient-based learning in complex systems. The advancements in accuracy and scalability can greatly benefit fields like systems biology and chemical kinetics, where precise simulations are crucial for understanding dynamic processes.
Key Takeaways
- Introduces a method that decouples forward simulation from backward differentiation for enhanced stochastic simulation.
- Achieves high accuracy with minimal error in complex models, including gene regulatory networks and ion channel dynamics.
- Demonstrates a GPU implementation capable of 1.9 billion simulation steps per second, matching non-differentiable simulators.
Quantitative Biology > Quantitative Methods arXiv:2602.19775 (q-bio) [Submitted on 23 Feb 2026] Title:Exact Discrete Stochastic Simulation with Deep-Learning-Scale Gradient Optimization Authors:Jose M. G. Vilar, Leonor Saiz View a PDF of the paper titled Exact Discrete Stochastic Simulation with Deep-Learning-Scale Gradient Optimization, by Jose M. G. Vilar and Leonor Saiz View PDF Abstract:Exact stochastic simulation of continuous-time Markov chains (CTMCs) is essential when discreteness and noise drive system behavior, but the hard categorical event selection in Gillespie-type algorithms blocks gradient-based learning. We eliminate this constraint by decoupling forward simulation from backward differentiation, with hard categorical sampling generating exact trajectories and gradients propagating through a continuous massively-parallel Gumbel-Softmax straight-through surrogate. Our approach enables accurate optimization at parameter scales over four orders of magnitude beyond existing simulators. We validate for accuracy, scalability, and reliability on a reversible dimerization model (0.09% error), a genetic oscillator (1.2% error), a 203,796-parameter gene regulatory network achieving 98.4% MNIST accuracy (a prototypical deep-learning multilayer perceptron benchmark), and experimental patch-clamp recordings of ion channel gating (R^2 = 0.987) in the single-channel regime. Our GPU implementation delivers 1.9 billion steps per second, matching the scale of non-differentia...
