Modeling Randomness with Neural Networks
![The study demonstrates how increasingly refined stochastic interpolation-using SINNOs with parameters [latex]n=5, 10, 20, 50[/latex]-can approximate the Ornstein-Uhlenbeck process, revealing that even simple numerical methods can converge towards the true stochastic path with sufficient refinement.](https://arxiv.org/html/2512.24106v1/SINNOsapprox.png)
Researchers have developed a new approach to accurately simulate and predict stochastic processes using a novel type of neural network operator.
![The study demonstrates how increasingly refined stochastic interpolation-using SINNOs with parameters [latex]n=5, 10, 20, 50[/latex]-can approximate the Ornstein-Uhlenbeck process, revealing that even simple numerical methods can converge towards the true stochastic path with sufficient refinement.](https://arxiv.org/html/2512.24106v1/SINNOsapprox.png)
Researchers have developed a new approach to accurately simulate and predict stochastic processes using a novel type of neural network operator.
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