A single neural operator can approximate the map from arbitrary joint densities to their conditionals, backed by new continuity results and illustrated on Gaussian mixtures.
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A Bayesian optimal experimental design framework with Gaussian approximation of expected information gain and surrogate Fisher information enables optimized uniaxial tests that significantly improve identifiability of history-dependent constitutive parameters over random designs.
LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.
Adaptive reduced-basis trust-region framework for efficient IRGNM-based defect identification in hyperbolic elastic systems, extending prior elliptic/parabolic work.
citing papers explorer
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One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators
A single neural operator can approximate the map from arbitrary joint densities to their conditionals, backed by new continuity results and illustrated on Gaussian mixtures.
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Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws
A Bayesian optimal experimental design framework with Gaussian approximation of expected information gain and surrogate Fisher information enables optimized uniaxial tests that significantly improve identifiability of history-dependent constitutive parameters over random designs.
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LEIA: Learned Environment for Interactive Architected Materials
LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.
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Adaptive Reduced-Basis Trust-Region Methods for Defect Identification in Elastic Materials
Adaptive reduced-basis trust-region framework for efficient IRGNM-based defect identification in hyperbolic elastic systems, extending prior elliptic/parabolic work.