MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems | ScienceToStartup | ScienceToStartup

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References (44)

[1]

Meta-modelling paths of simple climate models using neural networks and dirichlet polynomials: an application to DICE

2025Emmanuel Gobet, Yushan Liu et al.

[2]

A foundation model for the Earth system

2025Cristian Bodnar, W. Bruinsma et al.

[3]

Adaptive Movement Sampling Physics-Informed Residual Network (AM-PIRN) for Solving Nonlinear Option Pricing models

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"A novel approach to uncertainty quantification in dynamical systems using MCMC-informed neural emulators."

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MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems

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References (44)

Founder's Pitch

"A novel approach to uncertainty quantification in dynamical systems using MCMC-informed neural emulators."

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MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems

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References (44)

Founder's Pitch

"A novel approach to uncertainty quantification in dynamical systems using MCMC-informed neural emulators."

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