Berger, Julien Dutykh, Denys Mendes, Nathan Rysbaiuly, Bolatbek

This work presents a detailed mathematical model combined with an innovative efficient numerical model to predict heat, air and moisture transfer through porous building materials. The model considers the transient effects of air transport and its impact on the heat and moisture transfer. The achievement of the mathematical model is detailed in the...

Berger, Julien Dutykh, Denys Mendes, Nathan Rysbaiuly, Bolatbek

This work presents a detailed mathematical model combined with an innovative efficient numerical model to predict heat, air and moisture transfer through porous building materials. The model considers the transient effects of air transport and its impact on the heat and moisture transfer. The achievement of the mathematical model is detailed in the...

Berger, Julien Dutykh, Denys Mendes, Nathan Rysbaiuly, Bolatbek

This work presents a detailed mathematical model combined with an innovative efficient numerical model to predict heat, air and moisture transfer through porous building materials. The model considers the transient effects of air transport and its impact on the heat and moisture transfer. The achievement of the mathematical model is detailed in the...

Fadili, Jalal M. Garrigos, Guillaume Malick, Jérôme Peyré, Gabriel

Low-complexity non-smooth convex regular-izers are routinely used to impose some structure (such as sparsity or low-rank) on the coefficients for linear predictors in supervised learning. Model consistency consists then in selecting the correct structure (for instance support or rank) by regularized empirical risk minimization. It is known that mod...

Fadili, Jalal M. Garrigos, Guillaume Malick, Jérôme Peyré, Gabriel

Low-complexity non-smooth convex regular-izers are routinely used to impose some structure (such as sparsity or low-rank) on the coefficients for linear predictors in supervised learning. Model consistency consists then in selecting the correct structure (for instance support or rank) by regularized empirical risk minimization. It is known that mod...

Fadili, Jalal M. Garrigos, Guillaume Malick, Jérôme Peyré, Gabriel

Low-complexity non-smooth convex regular-izers are routinely used to impose some structure (such as sparsity or low-rank) on the coefficients for linear predictors in supervised learning. Model consistency consists then in selecting the correct structure (for instance support or rank) by regularized empirical risk minimization. It is known that mod...

Fadili, Jalal M. Garrigos, Guillaume Malick, Jérôme Peyré, Gabriel

Low-complexity non-smooth convex regular-izers are routinely used to impose some structure (such as sparsity or low-rank) on the coefficients for linear predictors in supervised learning. Model consistency consists then in selecting the correct structure (for instance support or rank) by regularized empirical risk minimization. It is known that mod...

Fadili, Jalal M. Garrigos, Guillaume Malick, Jérôme Peyré, Gabriel

Low-complexity non-smooth convex regular-izers are routinely used to impose some structure (such as sparsity or low-rank) on the coefficients for linear predictors in supervised learning. Model consistency consists then in selecting the correct structure (for instance support or rank) by regularized empirical risk minimization. It is known that mod...

Fadili, Jalal M. Garrigos, Guillaume Malick, Jérôme Peyré, Gabriel

Low-complexity non-smooth convex regular-izers are routinely used to impose some structure (such as sparsity or low-rank) on the coefficients for linear predictors in supervised learning. Model consistency consists then in selecting the correct structure (for instance support or rank) by regularized empirical risk minimization. It is known that mod...

Marazzato, Frédéric Ern, Alexandre Mariotti, Christian Monasse, Laurent

We propose a new explicit pseudo-energy and momentum conserving scheme for the time integration of Hamiltonian systems. The scheme, which is formally second-order accurate, is based on two key ideas: the integration during the time-steps of forces between free-flight particles and the use of momentum jumps at the discrete time nodes leading to a tw...