Uncorrected Gaussian residual penalties in full-space sampling converge after marginalization to the graph-lifted reduced posterior multiplied by the inverse absolute determinant of the state Jacobian, requiring explicit determinant corrections for equivalence.
Inverse Problem Theory and Methods for Model Parameter Estimation
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The paper introduces a protocol-resolved framework for virological measurements, defining an observation operator that maps latent ensembles to observed data and recasting plaque assays as estimates of protocol-conditioned infectious concentration.
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.
QPCA-EnDCF is a deterministic ensemble data assimilation method that replaces stochastic observation perturbations with a spectrally regularized rank-κ update on whitened residuals, yielding better spread-skill and rank-histogram reliability than stochastic EnKF on Lorenz-96 in undersampled regimes.
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.
Turbulence model choice causes up to 250% differences in geometric sensitivities during airfoil shape identification from wake data, showing that closure consistency is required for reliable inverse results.
Extends tropical Fermat-Weber problems to non-finite data and supplies LP formulations for inverse problems using the one-infinity pseudonorm.
A graph neural network learns to simulate 1D sea ice floe collisions and trajectories using data assimilation on synthetic data.
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Constraint residuals, graph posteriors, and determinant-corrected full-space targets in Bayesian inverse problems
Uncorrected Gaussian residual penalties in full-space sampling converge after marginalization to the graph-lifted reduced posterior multiplied by the inverse absolute determinant of the state Jacobian, requiring explicit determinant corrections for equivalence.
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Experimental Collapse in Virophysics: Protocol-Resolved Observation, Inference, and Plaque-Assay Blindness
The paper introduces a protocol-resolved framework for virological measurements, defining an observation operator that maps latent ensembles to observed data and recasting plaque assays as estimates of protocol-conditioned infectious concentration.
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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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A Data-Consistent Approach to Ensemble Filtering
QPCA-EnDCF is a deterministic ensemble data assimilation method that replaces stochastic observation perturbations with a spectrally regularized rank-κ update on whitened residuals, yielding better spread-skill and rank-histogram reliability than stochastic EnKF on Lorenz-96 in undersampled regimes.
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Mechanisms of Misgeneralization in Physical Sequence Modeling
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
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A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery
A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
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Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariances
New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.
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Effect of Turbulence-Closure Consistency on Airfoil Identification
Turbulence model choice causes up to 250% differences in geometric sensitivities during airfoil shape identification from wake data, showing that closure consistency is required for reliable inverse results.
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Tropical Fermat--Weber Problems over Non-Finite Data and their Inverse Formulations
Extends tropical Fermat-Weber problems to non-finite data and supplies LP formulations for inverse problems using the one-infinity pseudonorm.
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Graph neural network for colliding particles with an application to sea ice floe modeling
A graph neural network learns to simulate 1D sea ice floe collisions and trajectories using data assimilation on synthetic data.