Presents a likelihood-free transport map learned by minimizing an averaged energy-distance objective to amortize Bayesian inference for inverse problems, including PDE-constrained cases with neural operator representations.
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2026 3verdicts
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A Bayesian filtering extension of statFEM assimilates sequential observational data into elastodynamic finite element models by modeling uncertainties as Gaussian random fields, advancing the state with a stochastic Newmark scheme, and approximating the non-Gaussian prior via perturbation to obtain,
FLUID uses a recurrent encoder to create a fixed-size summary of observations, then learns coupled forward and backward flows to approximate filtering distributions and recover smoothing paths for nonlinear dynamics, with support for extrapolation.
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Amortized Energy-Based Bayesian Inference
Presents a likelihood-free transport map learned by minimizing an averaged energy-distance objective to amortize Bayesian inference for inverse problems, including PDE-constrained cases with neural operator representations.
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Statistical finite elements for sequential data synthesis in solid dynamics
A Bayesian filtering extension of statFEM assimilates sequential observational data into elastodynamic finite element models by modeling uncertainties as Gaussian random fields, advancing the state with a stochastic Newmark scheme, and approximating the non-Gaussian prior via perturbation to obtain,
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FLUID: Flow-based Unified Inference for Dynamics
FLUID uses a recurrent encoder to create a fixed-size summary of observations, then learns coupled forward and backward flows to approximate filtering distributions and recover smoothing paths for nonlinear dynamics, with support for extrapolation.