26 citations · 45 across the 5 of their papers we have counts for
5 papers
GGLasso -- a Python package for General Graphical Lasso computation
Fabian Schaipp, Christian L. Müller, Oleg Vlasovets
We introduce GGLasso, a Python package for solving General Graphical Lasso problems. The Graphical Lasso scheme, introduced by (Friedman 2007) (see also (Yuan 2007; Banerjee 2008))…
Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks
Suryanarayana Maddu, Dominik Sturm, Christian L. Müller +1
We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as Physics Informed Neural Ne…
STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations
Suryanarayana Maddu, Dominik Sturm, Bevan L. Cheeseman +2
Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretiz…
Learning physically consistent mathematical models from data using group sparsity
Suryanarayana Maddu, Bevan L. Cheeseman, Christian L. Müller +1
We propose a statistical learning framework based on group-sparse regression that can be used to 1) enforce conservation laws, 2) ensure model equivalence, and 3) guarantee symmetr…
Stability selection enables robust learning of partial differential equations from limited noisy data
Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini +1
We present a statistical learning framework for robust identification of partial differential equations from noisy spatiotemporal data. Extending previous sparse regression approac…