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Found 7 papers, 7 papers with code
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows.
Learning Diffusion Priors from Observations by Expectation Maximization
Diffusion models recently proved to be remarkable priors for Bayesian inverse problems.
Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model
Data assimilation addresses the problem of identifying plausible state trajectories of dynamical systems given noisy or incomplete observations.
Score-based Data Assimilation
Data assimilation, in its most comprehensive form, addresses the Bayesian inverse problem of identifying plausible state trajectories that explain noisy or incomplete observations of stochastic dynamical systems.
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
In this work, we introduce Balanced Neural Ratio Estimation (BNRE), a variation of the NRE algorithm designed to produce posterior approximations that tend to be more conservative, hence improving their reliability, while sharing the same Bayes optimal solution.
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations.
Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference
In many areas of science, complex phenomena are modeled by stochastic parametric simulators, often featuring high-dimensional parameter spaces and intractable likelihoods.
