Structural identifiability analysis shows point sources restore identifiability for inferring spatial stochastic dynamics parameters from static snapshots, unlike distributed sources, with limits depending on modeling choices.
Solving Inverse Problems in Physics by Optimizing a Discrete Loss: Fast and Accurate Learning without Neural Networks
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A geometry-aligned bi-fidelity surrogate maps low- and high-fidelity wildfire solutions to a common domain for improved reduced-basis reconstruction, lower error near fronts, and practical uncertainty quantification.
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Identifiability Limits of Physics-Informed Inference for Spatial Stochastic Dynamics from Static Snapshots
Structural identifiability analysis shows point sources restore identifiability for inferring spatial stochastic dynamics parameters from static snapshots, unlike distributed sources, with limits depending on modeling choices.
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A geometry-aligned multi-fidelity framework for uncertainty quantification of wildfire spread
A geometry-aligned bi-fidelity surrogate maps low- and high-fidelity wildfire solutions to a common domain for improved reduced-basis reconstruction, lower error near fronts, and practical uncertainty quantification.