Search Results for author:
Found 4 papers, 1 papers with code
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces
While early QD algorithms view the objective and descriptor functions as black-box functions, novel tools have been introduced to use gradient information to accelerate the search and improve overall performance of those algorithms over continuous input spaces.
Constrained Physical-Statistics Models for Dynamical System Identification and Prediction
A common practice to identify the respective parameters of the physical and ML components is to formulate the problem as supervised learning on observed trajectories.
Differentiable Feature Selection, a Reparameterization Approach
The second one corresponds to the elimination of redundant information by selecting variables in a correlated fashion which requires modeling the covariance of the binary distribution.
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models.
