Pinned Repositories
bio-integration
Integration of multiplex biologiacal networks using pyNetMelt functionalities.
CEMRACS2021_ReducedModeling
ChetoPicRenamer
CityPollutionModeling
ConDiPINN
PHISICAL INFORMED NEURAL NETWORKS FOR SINGULARLY PERTURBED CONVECTION-DIFFUSION PROBLEMS
GoodAir
SinglePaged - Simple Jekyll template
L-ODEfind
PerplexityLab
Reproducible research, File management, Explore multiple conditions, Parallel, Remember, Avoid re-doing, Analysis, Make reports, Explorable reports, Carbon and energy footprint
pyNetMelt
Integration of networks to enhance semi-supervised learning algorithms over graphs.
ROMHighContrast
We consider a parametric elliptic PDE with a scalar piecewise constant diffusion coefficient taking arbitrary positive values on fixed subdomains. This problem is not uniformly elliptic, as the contrast can be arbitrarily high, contrarily to the Uniform Ellipticity Assumption (UEA) that is commonly made on parametric elliptic PDEs. We construct reduced model spaces that approximate uniformly well all solutions with estimates in relative error that are independent of the contrast level. These estimates are sub-exponential in the reduced model dimension, yet exhibiting the curse of dimensionality as the number of subdomains grows. Similar estimates are obtained for the Galerkin projection, as well as for the state estimation and parameter estimation inverse problems. A key ingredient in our construction and analysis is the study of the convergence towards limit solutions of stiff problems when diffusion tends to infinity in certain domains.
agussomacal's Repositories
agussomacal/ROMHighContrast
We consider a parametric elliptic PDE with a scalar piecewise constant diffusion coefficient taking arbitrary positive values on fixed subdomains. This problem is not uniformly elliptic, as the contrast can be arbitrarily high, contrarily to the Uniform Ellipticity Assumption (UEA) that is commonly made on parametric elliptic PDEs. We construct reduced model spaces that approximate uniformly well all solutions with estimates in relative error that are independent of the contrast level. These estimates are sub-exponential in the reduced model dimension, yet exhibiting the curse of dimensionality as the number of subdomains grows. Similar estimates are obtained for the Galerkin projection, as well as for the state estimation and parameter estimation inverse problems. A key ingredient in our construction and analysis is the study of the convergence towards limit solutions of stiff problems when diffusion tends to infinity in certain domains.
agussomacal/PerplexityLab
Reproducible research, File management, Explore multiple conditions, Parallel, Remember, Avoid re-doing, Analysis, Make reports, Explorable reports, Carbon and energy footprint
agussomacal/ConDiPINN
PHISICAL INFORMED NEURAL NETWORKS FOR SINGULARLY PERTURBED CONVECTION-DIFFUSION PROBLEMS
agussomacal/L-ODEfind
agussomacal/GoodAir
SinglePaged - Simple Jekyll template
agussomacal/pyNetMelt
Integration of networks to enhance semi-supervised learning algorithms over graphs.
agussomacal/bio-integration
Integration of multiplex biologiacal networks using pyNetMelt functionalities.
agussomacal/CEMRACS2021_ReducedModeling
agussomacal/ChetoPicRenamer
agussomacal/CityPollutionModeling
agussomacal/ElQuijoteVirtual
Y que ocurriría si una IA cualquiera se embriagara de leer tantas novelas de caballería y viniera a perder el seso?
agussomacal/MasteringCOVID19
In order to fight against COVID19 we must understad its behaviour. Here a library with notebook included to fit any model you want to try using the available data up todate.
agussomacal/NonLinearRBA4PDEs
Non Linear Compressive Reduced Basis Approximation for PDE’s
agussomacal/pdefind_latent
Toolbox for finding differential equations to model empirical data. If some variables are not observed just use higer order derivatives!
agussomacal/presentations
Code for used to create my presentation or usefull videos/images using manim and manim-slides python packages.
agussomacal/SubCellResolution