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kategorie
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podkategorie
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pojęcia
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pojęcia

Deep Neural Operator

Neural network architecture learning mappings between function spaces, capable of generalizing to different discretizations and boundary conditions in physical simulation.

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Reduced Order Model

Dimensionality reduction technique preserving the essential characteristics of the original system while significantly reducing the computational cost of simulations.

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Polynomial Chaos Expansion

Spectral representation method approximating a system's response by a linear combination of orthogonal polynomials, effective for uncertainty propagation.

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Active Learning for Surrogates

Adaptive sampling strategy intelligently selecting the most informative training points to improve the efficiency of surrogate models.

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Multi-fidelity Modeling

Approach combining simulations of different accuracies and computational costs to build accurate and resource-efficient surrogate models.

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Proper Orthogonal Decomposition

Modal decomposition method identifying the dominant modes of a physical system to build reduced models preserving the essential energy of the system.

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Radial Basis Function

Interpolation function using radially symmetric functions to construct response surfaces approximating multidimensional data in simulation.

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Metamodeling

Construction of simplified models (meta-models) capturing input-output relationships of complex simulations while drastically reducing computation times.

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Response Surface Methodology

Collection de techniques statistiques et mathématiques développant des modèles approximatifs de surfaces de réponse pour l'optimisation et l'analyse de sensibilité.

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Transfer Learning for Physics

Adaptation de modèles pré-entraînés sur des domaines physiques similaires pour accélérer l'apprentissage de nouveaux modèles de surrogation avec moins de données.

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