Publications related to Analyse I (partie 5) : Fonctions continues et fonctions dérivables, la fonction dérivée

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Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

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Morphological processes and potential for lacustrine delta restoration

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Analyse I (partie 5) : Fonctions continues et fonctions dérivables, la fonction dérivée

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Residual-based attention in physics-informed neural networks

Climbing into their Skin to Understand Contextual Protein-Protein Associations and Localizations: Functional Investigations in Transgenic Live Model Organisms

Efficient Continual Finite-Sum Minimization

Null energy constraints on two-dimensional RG flows

In Medio Stat Virtus: Combining Boolean and Pattern Matching

BPS invariants from p-adic integrals

Graph generative deep learning models with an application to circuit topologies

Hitting with Probability One for Stochastic Heat Equations with Additive Noise

Network-based kinetic models: Emergence of a statistical description of the graph topology

Morphological processes and potential for lacustrine delta restoration

Residual-based attention in physics-informed neural networks

Morphological processes and potential for lacustrine delta restoration

Climbing into their Skin to Understand Contextual Protein-Protein Associations and Localizations: Functional Investigations in Transgenic Live Model Organisms

BPS invariants from p-adic integrals

Graph generative deep learning models with an application to circuit topologies

Hitting with Probability One for Stochastic Heat Equations with Additive Noise

Network-based kinetic models: Emergence of a statistical description of the graph topology

Efficient Continual Finite-Sum Minimization

Null energy constraints on two-dimensional RG flows

In Medio Stat Virtus: Combining Boolean and Pattern Matching